Ultrasound in Chips Production: Enhancing Tuber Quality via Pre-Planting Seed Treatment

Abstract

Modern agriculture is seeking methods that reduce pesticide use while simultaneously providing high-quality raw materials. The aim of this innovative study was to determine how treating potato planting tubers with ultrasound in an aqueous medium (pre-sowing treatment) affects the subsequent quality of the raw material and the final product. A three-year field experiment was conducted using a split-plot design with three replicates, comparing traditional technology with a technology using ultrasonic treatment of seed potatoes. Eight edible potato varieties were studied. Sonication significantly improved the processing quality of the tubers. Tubers from treated seed potatoes had significantly lower reducing sugar content (0.02 to 0.1%, depending on the variety). As a result, chips produced from sonicated tubers exhibited a lighter color, improved overall aesthetics and flavor, and reduced discoloration and moisture staining. The results obtained suggest that ultrasonic treatment of seed potatoes is a highly effective, non-thermal method for increasing the value of raw materials used in food processing. This is a promising, innovative technology with significant application potential, supporting sustainable agriculture by improving the quality of tubers and the finished product (chips) at the source. In the future, it will be necessary to optimize sonication parameters and evaluate the economic potential of this technology.

1. Introduction

Modern agriculture faces a critical dilemma: how to meet the growing global demand for food while simultaneously reducing the use of chemicals and pesticides. The search for eco-friendly, non-invasive technologies to improve the quality of raw materials has become a priority in the agri-food sector. In this context, ultrasound (acoustic waves above 16–40 kHz) is emerging as an exceptionally promising tool [1,2,3,4,5]. Known for its applications in medicine and industry, ultrasound offers a unique combination of non-invasiveness, minimal environmental impact, and a proven ability to stimulate plant physiological processes [1,6,7,8,9,10]. This opens a new direction in agrotechnology: instead of treating, raw materials can be actively optimized at the source. In food processing, ultrasound is divided into two types:

• Low intensity (>100 kHz, <1 W/cm²)—used for non-invasive analysis of chemical composition [10].
• High intensity (18–100 kHz, >1 W/cm²)—used for tissue modification, emulsification and cleaning [10].

In the context of pre-sowing treatment of seed tubers, i.e., a procedure aimed at tissue modification and stimulation, high-intensity ultrasound is used.

The mechanism of action is based on the phenomenon of acoustic cavitation, generating locally extreme conditions (up to 5000 K and 100 MPa), which can affect heat–mass exchange (stable cavitation) or cause destructive implosions (transient cavitation) [1,10,11]. Ultrasonic cavitation is a key physical phenomenon that, through the formation and rapid collapse of bubbles in a liquid under the influence of ultrasonic waves, serves as an efficient process-intensification technique in agri-food processing [1,2,3,4,5].

The importance of cavitation in agri-food processing: The intense mechanical and thermal effects generated by the implosion of cavitation bubbles have a wide range of applications, dramatically improving food quality, efficiency, and safety:

Extraction intensification: Cavitation generates strong shear forces and micro-cracks that effectively disrupt the cell walls of plant or animal material. This facilitates the release and extraction of bioactive components (e.g., antioxidants and oils) from the matrix, which is crucial in the production of supplements and functional additives.

Structure and texture modification: This phenomenon is used to homogenize emulsions, soften meat by fragmenting fibers, and improve the structure of products during processes such as freezing by influencing crystal formation. Safety and stability: The cavitation process can aid pasteurization and sterilization because the locally generated high temperatures and mechanical forces are capable of destroying microorganisms and inactivating undesirable enzymes.

Product quality improvement: In general, cavitation improves the quality, cohesiveness, and uniformity of many food products, providing an energy-efficient and non-chemical alternative to traditional methods [6,7,11].

Modern intensive agriculture relies heavily on synthetic pesticides to protect crops. Although these chemicals effectively control pests and diseases, their widespread use raises serious concerns [6,7]. The accumulation of pesticide residues in food and the environment, potential threats to public health, and the negative impacts on biodiversity and soil health [7,12,13,14,15,16] have prompted the search for alternative and sustainable agrotechnical solutions. This global trend imposes a dual challenge for modern agriculture: reducing chemical use while simultaneously ensuring high-quality raw materials for the food industry. In potato processing, not only pesticide safety but also achieving optimal raw material parameters (e.g., low reducing sugar content) in an environmentally friendly manner is crucial. In this light, ultrasound represents a novel approach. This technology, known in the food industry for its cavitation effects and its ability to improve product quality [3], is being investigated as a method for modulating plant metabolism to improve the qualitative characteristics of tubers before planting [11,12,13,14,16]. Our research directly contributes to sustainable agronomy by proposing a non-chemical pretreatment method that can reduce dependence on chemical interventions throughout the crop cycle.

In the context of potato tuber physiology, two main periods or phases are most often distinguished, providing a framework for deeper, multi-stage transformations: Phase 1—Planting Physiology: Cold-Induced Sweetening and Germination. Sugar accumulation in potato tubers is strongly influenced by genetic factors, abiotic stress, and storage conditions [12,13,14,15]. One of the key physiological mechanisms is cold-induced sweetening (CIS), which occurs in mother tubers at low temperatures prior to planting. This process involves the hydrolysis of starch into simple sugars (primarily glucose and fructose), which serve as an essential energy source for initiating metabolic processes. In the context of planting physiology, this increased sugar level in mother tubers is desirable, as it stimulates and supports faster, more vigorous bud germination and early plant growth. Pre-planting factors such as cooling, as well as innovative methods such as sonication of tubers before planting, are being considered as tools to modulate plant metabolism, including the expression of genes related to sugar synthesis [11,12,13,14,16]. Phase 2—Progeny Quality: Analysis Objective and Knowledge Gap. Unlike mother tubers, the sugar level in progeny (marketable) tubers is critical for their storage quality and suitability for processing (e.g., French fries or chips). In this case, a low level of reducing sugars is definitely desirable. This is to prevent unfavorable Maillard reactions—a non-enzymatic browning process that leads to the formation of dark color and undesirable acrylamides during heat treatment [12,13,17]. It is precisely this post-harvest phase of progeny tuber quality that our analysis focuses on. The knowledge gap lies in the lack of studies assessing whether pre-planting treatments applied to mother tubers (e.g., sonication) can serve as an ecological method for metabolic programming of progeny tubers to maintain low sugar levels during post-harvest storage, thereby increasing their quality and technological value.

Their advantage is non-invasiveness, the lack of negative impact on the environment, and the possibility of stimulating physiological processes of plants [1,6,7,8,9,10]. Modern food processing technologies are also looking for solutions combining high product quality with the principles of sustainable development. In this context, ultrasound technology is gaining particular importance, which is increasingly used in various stages of food production—from pretreatment of raw materials to preservation processes [3,5,8,15,16]. Ultrasound (above 20 kHz) is widely used in medicine (e.g., organ imaging), the electronics industry (non-contact sensors), and even in nature (e.g., echolocation in bats) [10,11], and recently also in agrotechnics [1,2,3,4,5]. In the case of potatoes, the key challenge of processing is to control the content of reducing sugars, which participate in Maillard reactions at high temperatures, leading to undesirable browning of chips and deterioration of taste [6,11,12,13]. Previous studies indicate that the accumulation of sugars is influenced by genetic factors, abiotic stresses (water and thermal), and storage conditions [6,13,14,15]. Contrary to the current trend in sustainable agriculture, conventional methods for modifying these parameters usually involve chemicalization. Traditional chemical methods primarily involve the use of plant growth regulators (PGRs) and antisprouting agents during storage [12,13]. Plant growth regulators (PGRs) are applied before harvest (during cultivation). These compounds affect ripening, such as ethephon (ethylene—accelerates senescence and ripening) or maleic hydrazide (MH) (used to inhibit germination). The goal of these methods is to modify tuber physiology (either by accelerating or delaying physiological maturity). Applying growth regulators in the field (e.g., MH) is also one way to maintain low levels of reducing sugars in tubers. They also delay physiological tuber aging, which is associated with sugar accumulation during storage [4,5,10,17].

Ultrasound is a versatile, sustainable, and economical technology widely used in food processing for operations such as filtration, freezing, frying, sterilization, or cutting. As an alternative, a non-thermal method increases efficiency, extends shelf life, and preserves nutrients, while improving physicochemical and sensory properties of food [8,9,14,17,18]. Darsana and Sivakumar [10] emphasize the key applications of ultrasonics as pre-processing and treatment methods across various food sectors. A novel approach in the production of chips is the use of ultrasonics for the prevention of processing defects. Our study deliberately introduces a novel approach to this technology. We used ultrasound not during processing (post-harvest), but as a treatment to stimulate potato tubers before planting (pre-sowing). Low-frequency ultrasound (20–300 kHz) can modify plant metabolism, including the expression of genes related to sugar synthesis [11,14]. In the food industry, this technology is already used to improve the color and texture of fried products due to cavitation effects that occur in an aqueous environment [3].

The aim of this research is to evaluate and optimize the sonication of edible potato tubers as a preliminary pre-processing treatment aimed at improving the quality of the raw material and the sensory properties of the final product, which is consistent with the pursuit of sustainable food production.

Null hypothesis (H0): Sonication of potato tubers before planting has no significant effect on the quality parameters of the raw material or on the properties of the final product (chips) compared with traditional cultivation methods.

Alternative hypothesis (H1): Sonication of potato tubers before planting significantly improves the quality parameters of the raw material and translates into better sensory and visual characteristics of the final product, making this method an effective tool for sustainable agriculture and food processing.

2. Materials and Methods

2.1. Plant Material and Experimental Design

In southeastern Poland, from 2015 to 2017, a strict field experiment was conducted at the Uhnin Experimental Variety Testing Station (51°34′ N, 23°02′ E, 155 m a.s.l.). This study used a split-plot design with three replications. The first-order factor consisted of two technologies: traditional, without the use of ultrasound (control), and technology using ultrasound as a pre-planting treatment. The second factor in the experiment was the eight varieties of edible potatoes from four earliness groups: very early: ‘Denar’ and ‘Lord’; early: ‘Owacja’ and ‘Vineta’; medium early: ‘Satina’ and ‘Tajfun’; medium late: ‘Syrena’ and ‘Zagłoba’. For greater clarity, we will present the experimental design in table form and clearly define the tests and assumptions used. Experimental design (

Table 1. Detailed structure and parameters of the split-plot experimental design.

Element	Description
Design Type	Split-Plot Design
Replicates	3
Main Plot Factor (A)	Technology: 2 Levels (Traditional vs. Ultrasound)
Sub Plot Factor (B)	Variety: 8 Levels (Edible Varieties)
Number of Combinations	2 × 8 = 16
Total Number of Plots	16 Combinations × 3 Replicates = 48 Plots

):

2.1.1. Field Research

The field experiment was conducted in accordance with the methodology of research on the economic values of varieties in force at the Research Centre for Cultivar Testing (COBORU) in Experimental Stations [18]. The pre-crop for potatoes was spring cereal (spring barley). Tubers were planted in the third decade of April at a spacing of 67.5 × 37 cm; the harvest plot was 15 m², i.e., 60 potato plants were planted in two rows of 30 tubers, with three replications. Standard NPK fertilization was applied: 90 kg N, 90 kg P₂O₅, and 135 kg K·ha⁻¹. After planting the tubers, weeds were controlled through both mechanical and chemical means [19]. Potato pests and diseases, specifically early and late blight (Alternaria sp. and Phytophthora infestans), were managed according to good agricultural practices [20]. Potato tubers were harvested at the 99° BBCH scale. During harvest, 20 tubers (10 pieces with dimensions of 51–60 mm and over 60 mm) with a shape typical for a given variety, without visible external deformations and greening, were collected from each field experiment replicate, intended for testing chips [21]. In addition, 5.5 kg of tubers were taken from each plot at harvest to determine the content of soluble and reducing sugars [22].

2.1.2. Characteristics of Potato Varieties

The experiment included eight edible potato varieties (Solanum tuberosum L.) with an established position in the consumer market [23,24]. These varieties represented four earliness groups: very early (‘Denar’ and ‘Lord’), early (‘Owacja’ and ‘Vineta’), mid-early (‘Satina’ and ‘Tajfun’), and mid-late (‘Syrena’ and ‘Zagłoba’).

The selected varieties were characterized by diverse properties that are crucial for processing:

Culinary type: Ranged from AB (‘Denar’, ‘Lord’, and ‘Vineta’) to B (‘Satina’, ‘Syrena’, and ‘Zagłoba’) and B–BC (‘Owacja’ and ‘Tajfun’). Starch content: This varied significantly, from the lowest in ‘Denar’ (12.3%) to the highest in ‘Tajfun’ (16.5%) [24].

Flesh color: The cultivars had flesh ranging from light yellow (‘Denar’, ‘Lord’, and ‘Owacja’) to yellow (‘Vineta’, ‘Satina’, ‘Tajfun’, ‘Syrena’, and ‘Zagłoba’).

The diversity of the traits studied, including earliness and starch content, highlights the deliberate differentiation of material to assess the adaptability of ultrasonic technology [18,23] (

Table 2. Key morphological and processing characteristics of the potato varieties tested.

Variety	Group of Earliness	Culinary Type	Starch Content (%)	Flavor Score (9-Point Scale)	Flesh Color
Denar	Very early	AB	12.3	7.0	Light yellow
Lord	Very early	AB	12.4	7.0	Light yellow
Owacja	Early	B-BC	13.5	7.0	Light yellow
Vineta	Early	AB	13.7	7.0	Yellow
Satina	Mid-early	B	12.8	7.5	Yellow
Tajfun	Mid-early	B-BC	16.5	7.0	Yellow
Syrena	Mid-late	B	15.4	7.0	Yellow
Zagłoba	Mid-late	B	12.6	7.0	Yellow

Source: own based on [18,23,24].

).

2.2. Ultrasonic Treatment (Sonication)

In the ultrasound-assisted technology, before planting, potato tubers were subjected to a sonication process in a water environment at a temperature of 18 °C for 10 min. Pilot studies were used to determine the optimal ultrasound exposure time, as prolonged exposure to ultrasound damaged the potato tuber’s ability to germinate. The sonication process was carried out in a tank device (Figure 1) equipped with three piezoelectric ultrasonic transducers mounted on the bottom of a stainless-steel tank. These transducers generated an ultrasonic wave at a fixed frequency of 40 kHz, in order to eliminate the effect of water on the physiological emergence of potato plants.

Number of tubers per batch: Each sonication batch consisted of approximately 180 seed tubers (for each variety and replicate). Tuber weight and tub volume: With an average seed potato weight of approximately 50 g, the total batch weight was approximately 9 kg. These tubers were treated in a 30-L ultrasonic bath. Effect on power density (W/L): This information is essential for accurately calculating the actual acoustic power density (W/L).

The sonication process was conducted in a tank device with eight transducers (ϕ50 mm) with a total output power of 700 W. Power density calculations, performed to ensure process repeatability, used a water volume of 30 L. Volumetric Power Density (X) = 23.3 W/L. Surface Power Density (Y) = 4.46 W/cm².

The system was powered by alternating current with a frequency of 40 Hz and an output power of 200 W, in accordance with the Polish Standard PN-IEC 6003 [25]. The low-frequency ultrasound (typically 20–100 kHz) used in this process operates through acoustic cavitation and the generation of mechanical and thermal effects. This is crucial for stimulating metabolic processes in the tubers [8,10]. Short exposures are designed to

−

Increase the permeability of tuber cell membranes, which promotes faster gas exchange and water absorption [8,11].

−

Stimulate enzymes and gene expression associated with early growth and carbohydrate metabolism [11], which has a direct impact on the subsequent reducing sugar content in the crop.

Short exposures are intended to stimulate metabolic processes in the tubers. This is crucial because sonication modulates enzymes and gene expression related to carbohydrate metabolism [C], which directly influences the subsequent, desirable reducing sugar content in the final yield.

Potato tubers grown in traditional technology were soaked in distilled water for 10 min to create an environment similar to that prevailing in the case of ultrasonic treatment.

2.3. Environmental Conditions

2.3.1. Soil Conditions

Table 3. Content of available forms of phosphorus, potassium, and magnesium, and some micronutrients, content of humus, and soil acidity before establishing the experiment in 2015–2017.

Year of Research	Content Macronutrients [mg·kg−1 Soil]			Humus Content [g·kg−1]	pH [KCL]	Micronutrients Content [mg·kg−1 Soil]
	P	K	Mg			Cu	Mn	Zn	Fe	B
2015	8.9	10.9	7.8	0.94	5.9	7.51	318	40.1	3760	7.24
2016	8.3	9.1	7.0	1.06	5.8	4.92	337	56.7	3925	5.28
2017	10.6	9.8	6.3	1.03	6.6	8.99	166	41.1	3600	6.04
Mean	9.3	9.9	7.0	1.02		7.02	273.8	45.96	3761.7	6.17

Source: Analyzed by the District Chemical and Agricultural Station in Lublin.

shows the chemical characteristics of the soil before the experiment was established in 2015–2017.

The assessment of physicochemical parameters of the soil was performed according to the standard analytical methods [25,26]. The experiment in Uhnin was conducted on Haplic Luvisols soils, classified as a good rye complex, quality class IVa [27]. The tested soil was characterized by proper pH and high content of most nutrients. However, low humus content could limit water and mineral retention. Variable potassium content (the lowest in 2016) could affect yields in drier years. High zinc and boron contents were beneficial for tuber quality, especially for the production of chips. These data suggest that the soil was sufficiently fertile, but in dry years, it could require additional irrigation or potassium fertilization. The high phosphorus and magnesium content minimizes the risk of deficiencies of these nutrients (Table 3) [28].

2.3.2. Meteorological Conditions

Meteorological conditions in the study years varied (

Table 4. Total rainfall and average air temperature during the growing season of potato, according to the meteorological station in Uhnin, 2015–2017.

Year	Month	Rainfall [mm]				Air Temperature [°C]
		Decade			Month	Decade			Mean
		1	2	3		1	2	3
2015	April	14.6	5.9	41.3	61.8	5.4	8.6	12.4	8.8
	May	23.4	13.9	83.0	120.3	12.6	12.0	13.7	12.8
	June	5.4	16.5	24.8	46.7	17.7	16.3	16.1	16.7
	July	10.5	21.6	13.1	45.2	19.6	18.7	19.9	19.4
	August	0.4	0.00	5.7	6.1	23.4	20.6	20.3	21.4
	September	32.4	32.6	65.2	130.2	16.0	17.7	12.8	15.5
	Total				410.3
2016	April	11.5	22.2	13.4	47.1	10.9	10.1	9.0	10.0
	May	4.9	2.8	38.6	46.3	14.4	17.8	12.9	15.3
	June	10.1	43.2	34.0	87.3	16.6	17.5	23.0	19.1
	July	22.4	30.8	60.9	114.1	19.5	20.1	21.9	20.5
	August	22.8	17.7	0.5	41.0	20.7	17.1	20.4	19.5
	September	7.6	0.10	4.1	11.8	19.5	15.5	11.5	15.5
	Total				347.6
2017	April	6.4	7.2	38.2	51.8	10.6	6.8	6.9	8.1
	May	45.1	1.3	19.1	65.5	10.5	13.0	17.4	13.7
	June	1.9	9.2	12.0	23.1	16.6	17.7	20.7	18.3
	July	10.1	80.9	41.0	132.0	17.9	19.0	21.0	19.4
	August	0.4	24.4	2.2	27.0	22.8	21.3	17.1	20.3
	September	38.7	35.9	8.7	83.3	16.3	15.3	12.8	14.8
	Total				382.7

Source: the meteorological station in Uhnin.

).

The highest total rainfall during the three-year study period of the potato vegetation was recorded in 2015. However, the rainfall distribution during this period was not conducive to the accumulation of tuber yield per unit area. During the period of intensive potato plant growth and the accumulation of tuber yield, in the period of June–August, a significant rainfall deficit was observed. September turned out to be very humid and very warm (2.8 °C) above the multi-annual average, but the rainfall of this month was no longer decisive for tuber yield (Table 4). Overall, 2015 was the wettest season (HTC = 1.7), especially in May (HTC = 3.1) and September (HTC = 2.8), but August was exceptionally dry (HTC = 0.1) [29]. The lowest total rainfall, but with a favorable distribution of rainfall during the potato vegetation period, was recorded in the second year of the study (2016), and this year turned out to be very warm. Water shortages were recorded only in August and September, in which total rainfall was only 60% of the multi-annual average, with an air temperature higher than normal by 1.7 °C (Table 4). The year 2016 was characterized by balanced to dry conditions (HTC = 1.3), and July was the wettest month (HTC = 1.9) [29]. Meteorological conditions in 2017 were highly variable. April turned out to be very humid. In May, the optimal amount of rainfall was recorded, while in June, a significant deficit was observed, receiving only 33% of the multi-annual average, and it was also characterized by an average air temperature 1.5 °C above normal. The rainfall balance improved only in July; a rainfall deficit was recorded again in August (Table 4). The year 2017 showed high variability in the hydrothermal coefficient—July was very wet (HTC = 2.3), while June and August were dry (HTC = 0.4) [29]. We will clearly state that no additional artificial irrigation was used. Cultivation relied solely on natural rainfall. This fact emphasizes that the observed quality improvements were achieved under natural, sustainable agricultural conditions, significantly strengthening the claims regarding the practical applicability and robustness of this technology.

2.4. Materials, Reagents, and Chemicals

2.4.1. Plant Materials and Chemicals

The plant material consisted of eight edible potato varieties purchased from a certified supplier. All chemical reagents used in laboratory analyses (e.g., sugar determinations) were provided by STANLAB (Olszewskiego 13 str., 20-481 Lublin, Poland).

2.4.2. Apparatus and Equipment

All experimental and analytical procedures were performed using the following equipment:

Tuber Sonication Device: Ultrasonic treatment of tubers (Figure 1) was performed in a tub device with two piezoelectric transducers, manufactured by POLSONIC (ul. Gidzińskiego 1b, 02-293 Warsaw, Poland). (Full technical specifications, including frequency and power density, are provided in the text describing the sonication method.)

Laboratory Dryer: Used to determine dry matter, manufactured by DANLAB (ul. Handlowa 6D, 15-399 Białystok, Poland).

Fryer: A 6 L, single-chamber electric fryer (3 kW, 230 V), model FG09006, manufactured by FORGAST (ul. Owsiana 58a, 40-780 Katowice, Poland), was used to fry the chips.

2.5. Post-Harvest and Laboratory Analyses

2.5.1. Chips Rating

The tubers intended for chips were peeled and then cut into 1.0–1.5 mm-thick slices. They were then rinsed with cold water to rinse out free starch and dried. Chips were fried using a single-stage frying method in vegetable oil heated to 170 °C for 3–4 min, until the moisture content dropped below 2% (Figure 2).

Immediately after frying, the color, taste, and general appearance of the products, as well as their defects, i.e., discoloration and so-called wet spots, were assessed. The color of the chips was assessed on a 9° scale using the “Cards color” table developed by the European Potato Research Association in Wageningen, where 9°—light color; 8°—gold color; 7°—light gold color; 6°—dark gold color; 5°—brown-gold color; 4°—brown color; 3°—dark brown color; 2°—brown color; and 1°—burnt, brown-black color. The desired color of the chips was in the range of 6–9° on a 9° scale. In addition, a visual and organoleptic evaluation of the chips was performed on a 5-point scale, where 5.0–4.5° means a very good result; 4.4–4.0°—good; 4.0–3.0°—satisfactory; 3.0–1.0°—unsatisfactory [24,25]. The visual, taste, and aroma evaluation of the chips was performed by a trained and experienced 10-person team, meeting formal and consumer requirements for organoleptic and rheological evaluation, in accordance with the EN ISO 8586 standard [25]. Each analysis was performed in 10 replicates.

2.5.2. Determination of Sugars

Technique: Iodometric Luff–Schoorl method with modification [30]. Standards: Glucose standard solutions were used for calibration. Sample preparation: Fresh potato tubers were homogenized and extracted in distilled water (80 °C, 30 min). The extract was clarified via filtration and diluted to the desired volume. For each sample, three independent replicates (n = 3) were prepared.

Calculations: Sugar content was expressed as reducing sugars [%] using the following equation:

$${\displaystyle \frac{\left(Vo-V\right)\times c\times k\times 100}{m}},$$

(1)

where V_o—blank volume, V—sample volume, c—Na₂S₂O₃ concentration, k—conversion factor, and m—sample mass. Quality control: Each series was analyzed with a blank and a positive control (glucose solution). An RSD of <5% was assumed for replicates. Results are expressed as the mean ± SD of three replicates.

2.5.3. Fat Content Determination

The fat content in the chips was determined using the Soxhlet extraction method—gravimetric—while the moisture content was measured gravimetrically [26]. The advantage of this method was high accuracy and good repeatability (RSD < 2%).

2.5.4. Moisture Content Determination

Moisture content determination was performed using the thermogravimetric method (drying to constant mass) [26]. The advantages of this method were simplicity of execution, low cost, and RSD ~1–1.5% [26].

2.6. Statistical Calculations

The obtained research results were statistically analyzed based on a Two-way ANOVA model (Analysis of Variance), considering the split-plot design structure with three replications, which allowed for the use of separate errors for the main plots (Technology factor) and subplots (Variety factor). ANOVA calculations were performed using SAS, version 9.2 [31]. Before conducting ANOVA, model assumptions were verified. The normality of the residual distribution was assessed using the Shapiro–Wilk test. Homogeneity of variance was checked using Levene’s test [32]. The results confirmed that the residuals met the assumptions of parametric procedures. The statistical significance of factors and their interactions was verified at the level of α = 0.05. The statistical significance of the factors and their interactions was assessed using the Fisher–Snedecor F test at a level of α = 0.05 [32]. To quantitatively assess the practical significance of the analyzed factors, the effect size (e.g., partial eta-square) was also calculated. Tukey’s multiple T tests were used to perform pairwise comparisons of means and determine statistically homogeneous groups (indicated by letters) [32]. In addition, this paper presents descriptive statistics. Pearson’s r coefficient was used to calculate the correlation matrix, and a heat map was generated using appropriate software (IBM SPSS Statistics version 30) [33].

Software: The following analytical programs were used to analyze the results: SAS, version 9.2 [31] and IBM SPSS Statistics version 30 [33].

3. Results

3.1. Evaluation of the Quality of Chips

The use of ultrasound as a pre-planting treatment in potato cultivation contributed significantly to the improvement of the quality of chips in parameters such as color, visual evaluation, and organoleptic evaluation, in relation to chips made from tubers obtained using traditional cultivation technologies. Statistical analysis was performed at a significance level of α = 0.05 (

Table 5. Influence of cultivation technology, varieties, and years on the quality of chips.

Experimental Factors		Chip Evaluation Parameters
		Color on a 9º Scale	Visual Assessment on a 5º Scale	Organoleptic Evaluation on a 5º Scale
Technologies	Traditional	6.79 a*	3.60 a	3.72 a
	Ultrasounds	7.50 b	4.18 b	4.22 b
Varieties	‘Denar’	5.94 a	3.33 ba	3.44 ba
	‘Lord’	6.34 ba	3.06 a	3.31 a
	‘Owacja’	6.42 ba	3.28 ba	3.08 a
	‘Vineta’	7.96 d	4.50 d	4.53 d
	‘Satina’	7.28 c	3.94 c	4.0 c
	‘Tajfun’	8.36 d	4.72 d	4.78 d
	‘Syrena’	8.17 d	4.61 d	4.81 d
	‘Zagłoba’	6.70 bc	3.69 bc	3.83 bc
Years	2015	7.69 c	4.11 c	4.34 b
	2016	6.53 a	3.68 a	3.75 a
	2017	7.22 b	3.89 b	3.82 a
Mean		7.15	3.89	3.97

* The letter indices accompanying the arithmetic meaning represent so-called homogeneous groups. The occurrence of the same letter index for at least one of the means indicates the lack of a statistically significant difference between them. The subsequent letter indices (a, b, c, d, etc.) define groups of means in ascending order. Means marked with the same lowercase letter within a column are not statistically significantly different at the assumed significance level of α = 0.05 (Tukey’s test).

).

The genetic properties of the tested varieties significantly modified the discussed features of the chips. The lightest color of the chips, the highest visual assessment, and the highest organoleptic assessment were noted in chips made from the following varieties: Tajfun’, Syrena’, and ‘Vineta’, which were placed in one homogeneous group. The darkest color was noted in chips made from tubers of the ‘Denar’ variety, while the lowest visual assessment on a 5° scale was obtained for chips made from tubers of the ‘Lord’ variety. This variety also had the lowest organoleptic assessment, similarly to chips made from the ‘Owacja’ variety (Table 3). Meteorological conditions during the years of this study significantly modified the desired quality parameters of the chips. The agrometeorological conditions of the first year of this study, which were characterized by a deficiency of rainfall, significantly contributed to obtaining chips with the lightest color and at the same time obtained the highest visual and organoleptic assessments, in relation to the other years of this study. In the rainy year of 2016, the darkest chips were obtained, and they were characterized by the lowest visual and organoleptic assessments (Table 5).

The applied cultivation technologies had a significant effect on discoloration, the occurrence of wet spots, and fat content in the chips but did not affect the moisture content of this product. The use of ultrasound technology contributed to reducing discoloration by half, as well as a significant reduction in wet spots and lower fat absorption by the chips. Statistical analysis was performed at a significance level of α = 0.05 (

Table 6. Influence of cultivation technology, variety, and years of cultivation on defects and fat content in chips.

Experiment Factors		Chip Evaluation Parameters [%]
		Discoloration	Humidity	Moist Areas	Content of Fat
Technologies	Traditional	9.35 b*	2.19 a	3.43 b	28.25 b
	Ultrasounds	4.64 a	2.28 a	1.78 a	27.27 a
Cultivars	‘Denar’	6.17 ba	2.44 ab	1.72 abc	24.30 a
	‘Lord’	19.61 c	3.19 b	5.67 e	25.30 ab
	‘Owacja’	9.83 b	2.16 ab	3.11 dbc	25.90 b
	‘Vineta’	2.44 a	1.92 a	0.28 a	26.75 b
	‘Satina’	8.61 b	2.26 ab	4.78 de	27.48 b
	‘Tajfun’	2.78 a	1.96 a	0.56 a	29.42 b
	‘Syrena’	2.72 a	1.87 a	0.83 ab	31.25 b
	‘Zagłoba’	3.78 a	2.08 ab	3.89 dec	31.68 b
Years	2015	2.88 a	1.90 a	0.60 a	27.06 a
	2016	8.92 b	2.13 ab	3.27 b	27.81 a
	2017	9.19 b	2.68 b	3.94 b	27.62 a
Mean		6.99	2.24	2.60	26.39

* The letter indices accompanying the arithmetic meaning represent so-called homogeneous groups. The occurrence of the same letter index for at least one of the means indicates the lack of a statistically significant difference between them. The subsequent letter indices (a, b, c, d, etc.) define groups of means in ascending order. Means marked with the same lowercase letter within a column are not statistically significantly different at the assumed significance level of α = 0.05 (Tukey’s test).

).

Genetic features proved to be a factor significantly influencing all the tested defects and fat content in the chips. The lowest fat content was recorded in chips made from tubers of the ‘Lord’ and ‘Denar’ varieties, while the remaining varieties had a similar fat content and were in the same homogeneous group (Table 4). Among the varieties studied, ‘Vineta’, ‘Syrena’, ‘Tajfun’, and ‘Zagłoba’ formed a homogeneous group with the lowest percentage of discolored chips. Chips made from these varieties, except for the ‘Zagłoba’ variety, were also characterized by the lowest moisture content. Chips made from tubers of the ‘Vineta’ and ‘Lord’ varieties had the fewest moist spots and generally had the worst quality parameters. They were characterized by the highest share of discoloration, the highest moisture content, and the moistest spots (Table 6). Chips obtained in the first year of this study, in which a significant water deficit was noted, were characterized by the best parameters. They had the lightest color, were the least moist, and had the fewest moist spots compared to chips from the other years of this study (Table 6).

3.2. Content of Total Sugars and Reducing Sugars

Studying the content of total sugars and reducing sugars in tubers of different potato varieties allows for the assessment of their suitability for processing, especially for the production of chips and French fries (Figure 3).

Total Sugars: Total sugar content ranged from 0.74% (‘Syrena’) to 1.45% (‘Zagłoba’). The highest levels were recorded in ‘Zagłoba’, and the lowest in ‘Syrena’. High total sugar content may affect the sweet flavor of the tubers and their behavior during heat treatment (Figure 3). Reducing Sugars and Processing: Reducing sugars are crucial in the frying process because they influence the Maillard reaction, which is responsible for the darkening of French fries and chips. The lowest levels of these sugars were found in the following varieties: ‘Tajfun’ (0.29%), ‘Syrena’ (0.31%), and ‘Vineta’ (0.48%). Thanks to their low values (below 0.5%), these varieties are best suited for chip production. The ‘Satina’ cultivar (0.54%) is borderline acceptable. The highest reducing sugar levels, which pose a risk of excessive darkening of the product, were observed in the following cultivars: ‘Zagłoba’ (0.74%), ‘Lord’, ‘Owacja’, and ‘Denar’ (less suitable for processing). Statistical Analysis (LSD): ‘Tajfun’ and ‘Syrena’ formed the first homogeneous group, while ‘Lord’ and ‘Owacja’ (0.62%) formed the second, indicating significant differences between the tested cultivars (Figure 3).

Impact of Technology: The technologies used had no significant effect on reducing sugar content. The exception was the second year of this study (with the highest rainfall), where a significant decrease in reducing sugar content was observed after sonication of tubers prior to planting (Figure 4).

The genetic properties of the studied varieties in interaction with the variable conditions of the years of the study significantly influenced the content of reducing sugars in potato tubers. The best raw material for the production of chips was obtained in the first and last year of the study. The content of reducing sugars in the fresh mass of tubers in these years did not exceed 0.5%, except for the ‘Zagłoba’ variety. The genetic characteristics of the studied varieties, in interaction with the varying conditions across the study years, significantly influenced the reducing sugar content in potato tubers. The best raw material for chip production was obtained in the first and third years of the study. In these years, the reducing sugar content in the fresh tuber mass did not exceed 0.5%, with the exception of the ‘Zagłoba’ variety. The first year was characterized by a moderate rainfall deficit, while the third year experienced both excesses and deficits in rainfall. In the second year, no significant differences were observed between the varieties, and most accumulated more than 1.0% reducing sugars, exceeding the permissible limit (Figure 5). The first year of the study was characterized by a certain deficiency of rainfall, while the last year was characterized by both excess and deficiency of rainfall. In the second year of the study, no significant differences were noted between the varieties. Most varieties accumulated more than 1.0% exceeded their permissible limit (Figure 5).

The applied technologies, in cooperation with the genetic properties of the tested varieties, in most cases, did not have a significant effect on the content of reducing sugars in potato tubers. Under the influence of sonication, a clear tendency towards a lower accumulation of sugars was observed in the tuber. In two cases, contrasting effects were observed. In the ‘Denar’ variety, a significant increase in the content of reducing sugars was noted in the sample where ultrasound was used compared with the traditional technology. In the second case, in the ‘Vineta’ variety, the opposite effect was observed, i.e., the applied ultrasonics contributed to a significant decrease in the content of reducing sugars in the fresh mass of tubers compared with the traditional technology (Figure 6).

3.3. Multivariate Statistical Analysis of Quality Characteristics of Potato Chips

Descriptive statistics for the chips and some characteristics of the raw material for chip production, such as sugar content, are presented in

Table 7. Descriptive statistics of the characteristics of chips and sugar content in the raw material for the production of chips.

Specification	y1	x1	x2	x3	x4	x5	x6	x7	x8
Average	7.15	3.89	3.97	2.24	6.99	2.60	27.59	1.05	0.53
Median	7.40	4.00	4.00	2.00	5.00	0.00	27.01	0.72	0.46
Standard deviation	1.41	0.92	0.93	1.24	9.76	4.11	2.69	0.76	0.33
Kurtosis	−0.72	0.18	0.27	78.76	7.50	3.96	−1.37	−1.10	−1.08
Skewness	−0.51	−0.71	−0.73	7.76	2.53	1.89	0.28	0.65	0.48
Range	5.20	4.00	4.00	14.00	50.00	20.00	7.99	2.41	1.06
Minimum	3.80	1.00	1.00	1.00	0.00	0.00	24.01	0.18	0.09
Maximum	9.00	5.00	5.00	15.00	50.00	20.00	32.00	2.59	1.15
Variation coefficient V (%)	19.71	23.54	23.29	55.52	139.52	157.79	9.74	72.86	63.15

y1—color of chips on a 9º scale; x1—visual assessment of chips on a 5º scale; x2—organoleptic assessment of chips on a 5º scale; x3—moisture content of chips in %, x4—defects and discolorations of chips in %; x5—moist spots in chips in %; x6—fat content in chips in %; x7—total sugar content in % of fresh weight of tubers; x8—reducing sugar content in % of fresh weight of tuber. Color and sensory evaluation of chips (y1, x1, and x2).

.

The average color value of chips (y1) was 7.15 on a 9-point scale, which suggests a good color for the final product. Both the visual evaluation (x1) and the sensory evaluation (x2) of chips oscillated around the values of 3.89–3.97 on a 5-point scale, which indicates a very high sensory quality of this product (Table 6). The coefficients of variation for these features (approx. 23%) indicate moderate sample variability, which in turn suggests relative homogeneity of the material (Table 7).

Product moisture and its defects (x3, x4, and x5). The moisture content of the chips (x3) was on average 2.2% and showed high variability (V = 55.5%) with an extreme deviation (max. 15%), which suggests significant differences in the water content of the raw material used for chips production (Table 7).

Defects and discolorations (x4) and wet spots (x5) were characterized by the highest variability (V = 139–158%) and strong skewness and kurtosis, which indicates the presence of extreme cases (e.g., samples with no defects at all or with a high proportion of them). The average value of defects was 6.9%, and wet spots of 2.6% (Table 7).

Fat content (x6). The average fat content in the chips was 27.59%, with relatively low variability (V = 9.74%), which indicates a homogeneous frying process. Low kurtosis and moderate skewness suggest a distribution close to normal (Table 7).

Sugar content in tubers (x7 and x8). The content of total sugars (x7) averaged 1.05%, and reduced sugars (x8) was 0.53% in the fresh weight of tubers. The high variability of these parameters (V = 73% and 63%, respectively) indicates significant differences among varieties, which is important because the content of reducing sugars strongly affects the intensity of darkening inf chips during frying. Too high content of these sugars (>0.5–0.6%) may lead to undesirable darkening of products (Table 7).

Most of the tested samples obtained high scores in the sensory evaluation, but the variability in the content of defects, spots, and reducing sugars, which affect the final quality of the chips, may be a major problem. The greatest threats to the quality of chips may be too high content of reducing sugars in the tubers of some varieties and the presence of defects, which indicates the need for careful selection of varieties and optimization of the conditions of storage and processing of tubers.

3.4. Correlations Between Potato Chip Quality Parameters—Statistical Analysis

Table 8. Pearson correlation matrix.

Specification	y1	x1	x2	x3	x4	x5	x6	x7	x8
y1	1.00
x1	0.87 **	1.00
x2	0.78 **	0.88 **	1.00
x3	−0.14	−0.25 *	−0.26 *	1.00
x4	−0.62 **	−0.71 **	−0.67 **	0.18	1.00
x5	−0.38 **	−0.54 **	−0.58 **	0.20	0.67 **	1.00
x6	0.34 *	0.38 **	0.43 **	−0.20	−0.30 *	−0.11	1.00
x7	0.44 **	−0.29 *	−0.28 *	−0.06	0.18	0.17	−0.04	1.00
x8	−0.54 **	−0.38 **	−0.37 **	0.01	0.22 *	0.19	−0.17	0.94 **	1.00

y1—color of chips on a 9º scale; x1—visual assessment of chips on a 5º scale; x2—organoleptic assessment of chips on a 5º scale; x3—moisture content of chips in %, x4—defects and discolorations of chips in %; x5—moist spots in chips in %; x6—fat content in chips in %; x7—total sugar content in % of fresh weight of tubers; x8—reducing sugar content in % of fresh weight of tubers; ** significant at p0.01, * significant at p0.05.

presents Pearson correlations, showing the relationships (strength and direction of dependence) between various features assessed in the study of potato chip quality and sugar content in the raw potato material. The purpose of the table is to determine which factors affect the quality of chips—including their color, appearance, taste, fat, and sugar content, as well as the occurrence of defects.

Using the correlation coefficient, we determine a numerical value that determines both the intensity and the nature (direct or inverse) of the relationship between two quantities. The color of the chips was characterized by an excellent positive correlation with the visual assessment and the organoleptic assessment of the chips. On the other hand, the color of the chips was strongly negatively correlated with the defects and discolorations of the chips (−0.62) and the content of reducing sugars in the fresh mass of the tubers (−0.54) (Table 8). The visual assessment of the chips was highly positively correlated with the organoleptic assessment of the chips (0.88) and highly negatively correlated with the defects and discolorations of the chips (−0.71) and the number of wet spots on the chips (−0.54). A strong negative correlation was observed between the organoleptic assessment of the chips and their defects, discoloration, and the number of wet spots. Specifically, the correlation coefficients were −0.67 for defects and discoloration, and −0.58 for wet spots. Our own research shows that defects and discoloration of chips are positively associated with the number of wet spots in chips; this positive linear correlation, unfavorable in the production of chips, reached a value of 0.67 (Table 8).

Sensory evaluation and color of chips (y1, x1, and x2). There was a very strong positive correlation between the color of chips (y1) and their visual evaluation (x1), as well as between color and organoleptic evaluation (x2) (r = 0.78). This means that the lighter and more uniform the chips, the better they were evaluated by tasters. The correlation between the visual and organoleptic evaluation of chips (x1 and x2) was also high (r = 0.88), which indicates their consistency in the perception of quality (Table 8).

Moisture content of chips (x3). The water content of chips (x3) showed weak relationships or no correlations with other features—the greatest negative correlations, but still weak, were observed with sensory evaluations (e.g., with x1: r = −0.25). This suggests that water content is not directly perceived by tasters and does not significantly affect the color or fat content of the chips (Table 8).

Defects and discoloration (x4), damp spots (x5). Defects and discoloration (x4) were strongly negatively correlated with sensory scores and chip color (r = −0.62 to −0.71), clearly indicating that increased defects reduce the quality of the chips. Damp spots (x5) also had a moderately strong negative correlation with sensory scores (r = −0.54 to −0.58) and were found to be moderately positively correlated with defects (r = 0.67), suggesting a common source of these undesirable features (e.g., technological or storage problems) (Table 8).

Fat content (x6). Fat content (x6) showed a moderate positive correlation with their sensory scores (e.g., r = 0.43 with x2), which may suggest that chips with higher fat content may be more acceptable in terms of taste and texture. At the same time, fat content did not correlate significantly with their moisture or with the sugar content of the raw material (Table 8).

Total sugars (x7) and reducing sugars (x8). A strong correlation between total and reducing sugars (r = 0.94) indicates their close interdependence, which is expected since reducing sugars are part of total sugars. Reducing sugars (x8) were significantly negatively correlated with the color of the chips (r = −0.54) and sensory scores, confirming that higher levels of them lead to darkening of the chips and their deterioration (Table 8).

The heatmap visually displays the strength and direction of correlation (Figure 7):

−

Warm colors (red) indicate a strong positive correlation (values closer to +1.00).

−

Cool colors (blue) indicate a strong negative correlation (values closer to −1.00).

−

Neutral colors (close to white) indicate no correlation (values closer to 0.00).

Among the presented correlations, strong correlations were selected. The heatmap reveals several key relationships, such as

Correlations of y1 with other attributes:

A strong positive correlation with x1 (visual assessment) (r = 0.87) and x2 (organoleptic assessment) (r = 0.78). This suggests that lighter-colored French fries are closely related to higher (better) visual and flavor assessments.

A strong negative correlation with x4 (defects and discoloration) (r = −0.62). The better the color, the fewer defects and discoloration.

A negative correlation with x8 (reducing sugars) (r = −0.54). Higher reducing sugar content (which is the main cause of darkening during frying via the Maillard reaction) is strongly correlated with poorer (darker) French fries’ color. Correlations between traits (e.g., predictors):

A very strong positive correlation between x1 (visual assessment) and x2 (organoleptic assessment) (r = 0.88). These scores are practically identical.

A very strong positive correlation between x7 (total sugars) and x8 (reducing sugars) (r = 0.94). Reducing sugar constitutes the vast majority of total sugars and is strongly correlated.

A strong positive correlation between x4 (defects) and x5 (damp spots) (r = 0.67). Damp spots are a significant component of defects and discoloration (Figure 7).

3.5. Principal Component Analysis (PCA)

PCA (Principal Component Analysis) is a dimensionality reduction technique that transforms the original variables into a new set of uncorrelated variables called principal components. PCA on our dataset (10 variables, including input X1–X6 and output Y1–Y4) required the following key steps:

• Data Preparation: Standardization of all 10 variables (averaging to 0, standard deviation to 1) was necessary because the variables differed in scale (e.g., Y2 vs. X1–X3).
• Calculation and Dimensionality Reduction: Calculation of eigenvalues (λ) based on the correlation matrix. Component Selection: The optimal number of Principal Components (PCs) was selected using the K1 Criterion (λ > 1) and the Scree Plot.
• Interpretation of Results: Variance Explained: The percentage of total data variability retained in the selected PCs (e.g., PC1 + PC2) was determined. Loadings: The coefficients (loadings) for each variable within PC1 and PC2 were analyzed to identify the components (e.g., PC1 = “Process Performance” and PC2 = “Input Conditions”).

Visualization (Biplot): A biplot was generated to verify the correlations between variables (vector direction) and the position of individual experiments (points).

Figure 8 shows the cumulative proportion of variance explained using successive principal components in a PCA analysis. The curve indicates that most of the variance is captured by the first few components, with diminishing gains beyond the sixth component. Dimensionality Reduction Conclusion: According to generally accepted standards (80% to 90% information retention), the optimal choice for dimensionality reduction is 3 or 4 components. Choosing 3 PCs: Explains over 80% of the variance, which is often considered sufficient. 4PC Selection: Explains almost 90% of the variance, offering a very robust representation of the original 10-variable space (Figure 8).

The Cumulative Explained Variance (CV) plot is crucial for determining the optimal number of Principal Components (PCs) that effectively represent the total information in the original dataset.

Analysis of the plot yielded the following results:

PC1: Explains approximately 47% of the total variance.

PC1 + PC2 (2 components): Together, they explain approximately 68% of the variance.

PC1 + PC2 + PC3 (3 components): Explains over 80% of the variance.

PC1 to PC5 (5 components): Cumulative variance reaches approximately 94% (Figure 9).

The variable loading plot in Figure 9 shows how the eight variables (X1–X8) correlate with the first two principal components (PC1 and PC2). PC1 (Horizontal Axis): This is the contrast axis. It separates the variables into two strongly correlated, opposing groups: (X1 and X2) with positive loadings (right) and (X7 and X8) with negative loadings (left). PC2 (Vertical Axis): It is strongly dominated by variable X5 (top), which explains independent variance relative to PC1. Correlations: Variables X1/X2 are highly correlated with each other, as are X7/X8. X3 and X4 are moderately negatively correlated with PC1.

Figure 10 shows the distribution of chip samples across two principal components (PC1 and PC2). PC1 (Horizontal Axis) is strongly correlated with quality (Y1):

The highest-quality samples (Y1 > 8.0) cluster on the right side of the graph (positive PC1 values). The lowest-quality samples (Y1 < 6.0) cluster on the left side (negative PC1 values). Clustering: The data demonstrate strong separation; PC1 clearly separates high-quality (right) and low-quality (left) samples. PC2 (Vertical Axis): It does not show a clear correlation with Y1 quality, but it does show some variation within groups, for example, separating medium-quality samples along the vertical axis (Figure 10).

The results of the principal component analysis (PCA) for the quality characteristics of the chips are presented in Figure 11. This connects observation points (chips) to variable vectors (features X1–X7), with color indicating quality Y1. PC1 (45% Variance): This axis is the main factor differentiating quality (Y1).

Right side (high Y1): It is associated with features X1 and X2 (e.g., sweetness and crunchiness). Left side (low Y1): It is associated with feature X7 (e.g., bitterness and defects). PC2 (20% Variance) represents an orthogonal dimension of taste. It is dominated by X5 and X6, which do not strongly correlate with quality but differentiate samples (top/bottom of the graph). Therefore, the quality of chips is primarily determined using the contrast between features X1/X2 and X7. PC1 is the most significant component and appears to be the main dimension differentiating chip quality (Figure 11).

4. Discussion

4.1. Mechanism of Action of Ultrasonic Immersion

In the context of our innovative pre-planting treatment, a similar mechanism is likely crucial for achieving positive effects on tubers. Immersing tubers in water during ultrasonic immersion causes acoustic waves not only to penetrate the tissue but also to intensely disrupt the water boundary layer on the tuber surface [34].

This phenomenon, according to the mechanism described earlier [1,2,3,4,5,6], minimizes resistance and facilitates exchange processes between the aquatic environment and the cortex cells of the tuber. This mechanical stimulating effect can

−

Increase water absorption (imbibition) and accelerate the activation of tuber metabolism [35].

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Stimulate enzymes and improve the transport of nutrients or signaling substances, influencing metabolic pathways (e.g., sugar synthesis), which ultimately translates into improved tuber quality during growth [1,3].

Studies on the effects of stress (including acoustic stress) on potato metabolic pathways were conducted by Palomino-Rincón et al. [34]. These studies are consistent with our observations and previous studies [35,36].

Micromechanical Effects on Cell Walls: In plant products such as potato tubers, the mechanism of cavitation induced by ultrasound (US) involves the formation, growth, and implosion of gas microbubbles in a liquid medium (e.g., soil water or cell fluid). This bubble implosion generates local micro-pressure spikes and micro-fluid jets. The mechanical effect of US on plant tissues is the destabilization and increased permeability of cell walls (the so-called sonoporation phenomenon) [37]. Increased cell permeability after US treatment (even at the pre-emergence stage of potato, where ultrasound could have affected tuber metabolism) is crucial because:

−

Increasing the permeability of membranes and cell walls facilitates subsequent processes, such as mass transfer. In the context of our study, pre-emergence US application could have initiated changes at the cellular level in tubers, affecting dry matter content, turgor, and the subsequent heat and mass transfer kinetics during frying.”

−

Cavitation and Frying Kinetics (i.e., post-processing): Cavitation has the most direct impact on the structure, which subsequently determines the quality of the fried product [35,36,37]. Facilitating Mass Transfer (i.e., water evaporation): In the frying process of chips, rapid water removal and replacement with oil (or maintaining low oil absorption) are crucial. If US pretreatment increased the porosity or permeability of the cortical tissue of potato tubers, this could significantly alter the process because of the following:

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During chip frying, ultrasound, even indirectly (through changes induced by pre-emergence), can influence the formation of microchannels within the tissue. This structural change facilitates faster water evaporation and shortens frying time while minimizing thermal degradation of undesirable components, such as acrylamide, and potentially limiting oil absorption (by shortening exposure to high temperatures). Surface and Texture Modification: In the case of chips, crispness is a key attribute. Cavitation mechanisms, through polymer reorganization and fiber orientation changes (as occurs, for example, in meat tissues), in plant products can influence [1,23,37].

−

Mechanical effects of cavitation can lead to local hydrolysis and depolymerization, changing the stiffness of starch and pectin in the potato tissue. This structural modification can translate into improved crispness and reduced hardness in the final product, i.e., chips, compared to the control [37].

Non-thermal Phenomenon and Frying Temperatures: It is worth contrasting this with high frying temperatures to emphasize that the key changes occurred before heat treatment. The action of US during frying is primarily a mechanical modification, not preheating, especially since chips are fried at a much higher temperature (approx. 150–180 °C). Although US is a non-thermal phenomenon, it generates a small temperature increase. However, this small thermal effect is neglected compared to the temperatures used during frying. Therefore, the benefits observed in chips are primarily the result of mechanical and structural changes caused by cavitation in plant cells, not preheating of the material [35,37]

In summary, the phenomenon of physical disruption of the boundary layer in an aquatic environment, induced by ultrasound, acts as a micro-massage that stimulates seed potato readiness for germination and growth. This allows us to transfer a solid physical foundation from one field (filtration) to a new one (biostimulation), thus strengthening the scientific credibility of our methodology and our research.

4.2. Effect of Sonification on Potato Metabolism and Chip Quality—Current Mechanisms and Practical Implications

Own studies prove that ultrasonic waves activate metabolic processes in potato plants. In the studies, based on pilot studies, the optimal time of sonication for tubers was assumed to be 10 min, which is consistent with the findings of Teixeira da Silva & Dobránszki [35], Guiné et al. [38], and Kutlu et al. [3]. This states that the use of acoustic sound or ultrasound under extreme conditions with too high frequency or for long periods of exposure is harmful and even fatal to plants, while milder conditions of sonication can improve the growth or development of plants and affect the antioxidant system and hormonal balance of plants. The results of the studies by Antunes-Rohling et al. [39] confirm that ultrasonic treatment (US) significantly affects the properties of potatoes before frying, and that optimal parameters can effectively reduce the content of acrylamide in chips. These authors showed that a temperature of 42 °C favors water absorption, especially at lower ultrasound powers, which may be due to partial denaturation of cell membranes that facilitates diffusion. Extraction of reducing sugars is most effective at 35 kHz and 92.5 W kg⁻¹ (31% reduction), which directly mitigates the Maillard reaction during frying [7,34]. Changes in colorimetric parameters (↓L, ↑a) confirm the relationship between US treatment and the reduction in non-enzymatic browning. A 90% reduction in acrylamide compared to untreated samples proves that sonication can be a key strategy for the production of healthier chips [34].

4.3. Optimization of Potato Chip Production Processes Using Ultrasonic Technology

Practical implications:

Further optimization is necessary for process scaling, especially the energy balance.

Our research and reports by Kutlu et al. [3] indicate that the use of ultrasound significantly improves the color of chips compared to traditional technology. The latest studies [9,39] prove that this effect results from the short-term activation of ROS pathways, leading to increased activity of antioxidant enzymes (SOD and CAT), modulation of expression of genes related to carbohydrate metabolism, and inhibition of non-enzymatic browning reactions through destabilization of polyphenol oxidase (PPO). At the same time, it has been shown that long-term exposure (>15 min) leads to degradation of cell membranes and unfavorable sensory changes [6,9]. The use of ultrasonic technology in potato chip processing offers producers and consumers a number of benefits that can significantly improve product quality, production efficiency, and environmental impact. There are also methods in which ultrasound is applied post-harvest, during processing itself. Palomino-Rincon et al. [34], applying ultrasound (28 and 40 kHz) to chips immersed in water before frying (post-harvest method), reported a positive effect on the quality of the produced chips (e.g., reduced oil absorption). Similar effects were observed by Lu et al. [40]. Therefore, while previous studies focus on the effects of ultrasound on the finished raw material/semi-finished product (through tissue modification to improve the frying/processing process), our study describes the preventive and innovative effects of ultrasound on seed potatoes to improve a key component of the tuber (sugars) already during the growth and development stages [7,41].

Prempeh et al. [11] refer to this approach for food processing in general. Their suggestions for the use of ultrasound in potato processing are presented below:

• Ultrasound can be used to pretreat seed potatoes, which can accelerate germination, improve nutrient absorption, and strengthen plant immunity. This can lead to more uniform growth and healthier tubers with the desired reducing sugar content.
• Optimization of Processing:

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  Washing and Cleaning: Ultrasound can assist in removing dirt from potato surfaces, which is more effective than traditional methods and can reduce water consumption.

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  Slicing: Ultrasonic pretreatment before slicing can affect potato texture, facilitate precise cutting, and minimize cell damage [40,42].

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  Blanching: The use of ultrasound during blanching shortens process time, improves inactivation of enzymes responsible for browning (e.g., polyphenoloxidase), and reduces nutrient loss [36].

  −

  Draining/Dewatering Before Frying: Ultrasound can increase the porosity of potatoes, which facilitates the removal of excess water before frying, shortening frying time and reducing fat absorption.

  −

  Frying: Ultrasound applied during frying can accelerate the process, resulting in crispy potatoes while reducing fat content. It can also contribute to even heat distribution and reduce acrylamide formation [7,34].

  −

  Reducing Product Darkening: Direct ultrasound exposure can help control the Maillard reaction, reducing excessive browning of chips, which is crucial for consumer acceptance [43].

• Improved Quality and Shelf Life of the Final Product: Ultrasound can affect the cellular structure of potato flesh, resulting in improved texture and the desired crispness of the chips.

Sustainable development. Reduced Energy and Water Consumption: Process optimization with ultrasound can lead to energy savings and reduced water consumption throughout the production cycle [43,44]. Waste Reduction: Improving raw material quality and improving production processes can reduce rejection and post-production waste [45].

Figure 12 shows the key applications of ultrasound technology in potato chip production, illustrating its impact at various stages—from stimulating potato growth, to optimizing processing, and improving the quality and shelf life of the final product. The diagram also highlights environmental and economic benefits, such as water and energy savings, and reduced chemical usage.

4.4. Effect of Potato Sonication on Sugar Metabolism

In the opinion of Sawicka et al. [6], Zgórska & Czerko [12,13], Pszczółkowski & Sawicka [15], Pszczółkowski et al. [16], Zgórska & Sowa-Niedziałkowska [43], and Zgórska & Grudzińska [46], the determination of reducing sugars in potatoes is crucial for the quality of processed products. Due to the high genotypic variability, Peraza-Alemán et al. [42] developed predictive models using near-infrared hyperspectral imaging (NIR-HSI). The best model (SNV-PLSR) achieved R² = 0.88 (calibration) and 0.86 (validation), and variable selection algorithms (CARS and iPLS) reduced the number of wavelengths to 2.65–3.57% without losing accuracy. Visualization of sugar distribution at the pixel level confirmed the usefulness of NIR-HSI for assessing the quality of raw materials used in chip production. The sugar content in potato tubers is strongly genotype-dependent [14,47,48,49], and sonication, in the opinion of Peraza-Alemán et al. [42], does not significantly alter their levels. The key factors influencing sugar accumulation are included in

Table 9. Key factors influencing sugar accumulation in potato tubers and their impact on processing quality.

Factors	Effect	Source
Weather conditions	↑ in cold and humid periods	[39,41,50,51]
Storage temperature	Optimum: 6–8 °C (↑ below 4 °C and above 10 °C)	[12,41,43]
Harvest time	July harvest → 5× higher sugar content than August	[43,52,53]

↑, means that the “effect” is stronger/bigger/more pronounced during cool and humid periods. Source: own.

.

According to industry standards [44], acceptable sugar levels are as follows: Chips: ≤1.5 g·kg⁻¹ (max. 2.5 g·kg⁻¹) and French fries: ≤2.5 g·kg⁻¹ (max. 5 g·kg⁻¹).

Mechanisms of Chip Quality Improvement: The action of cavitation is directly responsible for morphological changes in the potato structure, which is a fundamental prerequisite for achieving beneficial outcomes such as fat reduction and improvement of the organoleptic quality (taste, crispness, and color) of fried snacks (

Table 10. Ultrasonic cavitation: mechanism of action on snack foods.

Aspect of Quality	Impact of Cavitation	Consequences for the Product
Cellular Structure	Increased fracture area of the potato cell matrix.	Promotes the development of a more porous microstructure [34].
Oil Absorption	Facilitates water removal and modifies the microstructure (via porosity).	Reduced oil uptake (reduction of 21.85–30.27% when combined with acetic acid soaking, according to Lu et al. [40,53].
Texture	Intense mechanical action modifies the structure of the surface and the interior.	Creation of more crisps outer surface and texture improvement (reduced roughness and enhanced crispness) [36,40].
Color	Structural changes and better control over the frying process.	Cavitation affects the color of chips, allowing for a more vivid and attractive color [39,40].

).

The 9.5% improvement in color is attributed to the following: PPO inactivation via cavitation-induced protein denaturation [9,54,55]; Structural changes in tuber tissue: Increased porosity → faster water evaporation (↓ final moisture by 12%); Enhanced oil diffusion during frying [6,8,12,43,56,57].

Industrial Implications: Recent recommendations from the European Commission, Directorate-General for Health and Food Safety [44] highlight the following: Customized sonication parameters based on Cultivar (e.g., low-starch potatoes require shorter exposure); End-use considerations (chips vs. fries vs. puree); Synergy with emerging technologies such as Pulsed electric field (PEF) and Low-temperature plasma treatment [39].

Future Research Directions: Further studies should focus on molecular mechanisms of potato response to acoustic stress, optimization for different farming systems (conventional vs. organic), and integration with AI and NIR spectroscopy for raw material sorting [37].

Key Improvements: Present information in a concise and structured format (bullet points and table for key factors), Use stronger academic tone (precise terminology and passive voice where appropriate), enhance readability (logical flow and clear cause–effect relationships) and ensure consistent formatting (uniform units and proper citation style).

4.5. Effect of Ultrasonic Treatment on the Fat Content of Chips

The results of the conducted studies indicate a significant relationship between starch content and chip quality. Studies by Zhang et al. [8] on structural modifications of starch induced by ultrasound and the reduction of oil absorption during frying have shown, among others:

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Morphological changes in starch: Erosion of the surface of starch granules under the influence of ultrasound (confirmed in microscopic studies) led to the formation of a more compact internal structure in chips. Reduced water mobility in the raw material for the production of chips after US treatment limits moisture migration during frying [8,45].

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Modulation of physical properties of slices: Ultrasound treatment increases porosity while reducing the diameter of slices (by ~15–20% according to SEM measurements). This leads to the formation of a more uniform microstructure with a limited tendency to crack during heat treatment [8,45].

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Mechanisms of fat absorption reduction: The modified starch structure creates a barrier limiting oil penetration (confirmed via NMR measurements), while faster water evaporation (up to 25% faster) promotes the formation of a surface layer inhibiting absorption. This in turn leads to reduced surface viscosity of the raw material for chip production (rheological measurements) [36,45,46,58].

−

This results in practical implications, such as

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US treatment at 35–45 kHz for 5–8 min allows us to achieve a reduction in the final fat content by 18–22% [47].

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Synergistic effect with pre-blanching (additional 7–9% reduction) [45,46].

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Possibility to control the texture of the final product by modulating sonication parameters [8,36,46,59].

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Development prospects for the production of chips primarily involve the following:

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The need for optimization for different potato varieties (with different starch content).

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Research on scaling the process with consideration of energy efficiency [46].

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Glucose content in tubers is usually 80.5–97.6% of the coefficient of variation V for the brightness of French fries and 88.4–94.2% for the brightness of potato chips.

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The critical ranges of glucose content for acceptable products in French fries and chips based on color values (L* and a*) are 12–22 mg/100 g and 8–14 mg/100 g, respectively, for the tested varieties [45].

Palomino-Rincón et al. [34] proved that ultrasound is a versatile technology aimed at optimizing the frying process, which results in higher efficiency, better oil control, and maintenance of desirable sensory characteristics in line with consumer preferences (color and consistency). Finally, these authors [34] demonstrated that pretreatment with ultrasound at a frequency of 40 kHz significantly improves the technological and sensory quality of potato chips, which in turn may promote the value of the chips.

4.6. Influence of Varieties on the Quality of Chips

4.6.1. Key Parameters of Quality and Varieties for Chip Production

The latest studies confirm that chip quality is mainly influenced by several factors: reducing sugar content (the main factor of darkening via the Maillard reaction); dry matter, which determines the texture and crispness of chips; starch content and its structure, which affect fat absorption; and enzymatic activity (e.g., polyphenoloxidases responsible for browning) [14,41].

According to the conducted research, the ‘Tajfun’ variety is still the leader due to the low content of reducing sugars (<0.3%) and high dry matter content (>22%). ‘Syrena’ is a variety with stable parameters even under unfavorable climatic conditions (lower accumulation of sugars with temperature fluctuations). ‘Vineta’ variety can be recommended due to the uniform structure of the flesh, which limits uneven frying. ‘Satina’ variety requires strict storage control, but new research indicates that appropriate storage conditions (4 °C, humidity 90%) can maintain sugars at an acceptable level (<0.5%).

Varieties not recommended for chip processing: ‘Lord’ and ‘Owacja’—despite agrotechnical improvements, they showed high variability in sugar content, which makes quality control difficult. Variety ‘Zagłoba’, even with optimal storage, exceeds the permissible standards for reducing sugar content (>0.7%).

4.6.2. Indications for the Processing Industry

Recommendations for the Processing Industry in the Context of the Latest Knowledge:

Optimal Variety Selection: The Tajfun and Syrena varieties continue to demonstrate high reliability in terms of quality parameters of the raw material for the production of chips. However, in light of ongoing climate change and new research, other varieties may appear as promising alternatives, potentially demonstrating better tolerance to extreme growing conditions. Nevertheless, recommendation for specific varieties should consider the latest results from field trials across different regions and years, assessing their yield and quality stability under changing conditions [59].

Monitoring of Storage Conditions: Close monitoring of storage conditions remains crucial. The latest research confirms that even varieties with the best quality potential can degrade as a result of inappropriate temperature and humidity. It is worth considering the implementation of predictive systems based on modeling the impact of environmental conditions during the growing season on optimal storage parameters for a given batch of raw material. Further and broader integration of rapid, non-destructive measurement technologies, such as near-infrared (NIR) spectroscopy and hyperspectral imaging (HSI), is essential for routine assessment of reducing sugar content and other key quality parameters of raw materials before processing. Leveraging the latest advances in artificial intelligence (AI) and machine learning (ML) enables the creation of precise and efficient real-time quality assessment systems, which dramatically optimize the production process and minimize losses [43].

Holistic Approach to Quality: The latest research [citations integrating the impact of variety, agrotechnics, and storage on chip quality] confirms that variety is the foundation of chip quality, but its potential can only be fully exploited with the use of optimal agrotechnical practices (considering adaptation to climate change, e.g., precise irrigation and fertilization) and precise storage conditions. Progress in potato breeding, focused on traits useful for processing and resistance to abiotic stresses, must be coupled with the implementation of integrated quality management systems at every stage of the supply chain [7].

Implementation of Recommendations: Conscious and consistent implementation of the latest recommendations, based on solid scientific research and innovative technologies, is crucial for significant improving production efficiency, reducing cost, ensuring high and stable quality of chips, and increasing consumer satisfaction in the face of dynamically changing climatic and market conditions [42,45,46,47,48,49,50].

4.7. Influence of Climatic Conditions on the Quality of Raw Material for Chip Production

4.7.1. Influence of Temperature During the Vegetation Period

Our own research and literature data [43,45,48,49,50] clearly indicate that warm summers (average daily temperature > 20 °C) contribute to a decrease in the content of reducing sugars in potato tubers (<0.4% for the Tajfun variety and <0.5% for the Syrena variety). At the same time, a higher dry matter content (>22%) has a positive effect on the crispness of chips. A negative correlation (r = −0.72) between temperature and sugar accumulation was confirmed. Conversely, cool summers (average daily temperature <15 °C) result in an increase in the level of reducing sugars by up to 30–50% (e.g., up to 0.7% in the Satina variety), increasing the risk of uneven frying and darkening of chips as a result of the Maillard reaction. Research by Pszczółkowski et al. [16], Zgórska & Czerko [13], and Zgórska and Grudzińska [46] clearly confirms that low temperatures (both during cultivation and storage) lead to a phenomenon called “cold-induced sweetening” (CIS). In this process, starch, the main component of potatoes, is broken down into simple sugars, such as glucose and fructose. These sugars are the main precursors of potato chip darkening.

4.7.2. Extreme Weather Phenomena

Analysis of data from our own research from 2015–2017 and from previous studies [50,51,53,54] showed that heat waves (>30 °C) inhibit tuber growth but paradoxically may lead to a decrease in sugar content (due to accelerated conversion into starch). Nevertheless, the risk of tuber deformation increases, which negatively affects the uniformity of the chips produced. Conversely, early autumn frosts can cause a rapid increase in the sugar content of potatoes (up to 1% in the ‘Zagłoba’ variety), necessitating earlier harvests for late-ripening varieties. Many scientific publications [16,41,43,48] confirm a negative correlation between temperature and sugar accumulation in potatoes. This means that as the temperature increases, the content of reducing sugars decreases, and vice versa. High temperatures during potato vegetation (above 20 °C), particularly during tuber formation, help maintain low levels of reducing sugars. According to Hu et al. [56], Pedrosa et al. [52], and Grudzińska and Zgórska [57], this occurs because potatoes expend more energy on starch synthesis rather than its breakdown. As a result, tubers have lower sugar content and higher dry matter content, which is desirable for potato chip production.

4.8. Reality Evaluation Relationships

The analysis revealed important connections between different assessment methods.

Visual and sensory evaluations showed remarkable agreement (r = 0.88), suggesting consistent quality perception across evaluation methods. Both evaluation types were adversely affected by the following:

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Defects and discolorations (visual: r = −0.71; sensory: r = −0.67).

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Moisture spots (visual: r = −0.54; sensory: r = −0.58);

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Production Quality Concerns [8,47].

The high correlation (r = 0.88) between visual and sensory assessments is widely confirmed in food research [15,16,42,53]. Appearance, color, and the absence of surface defects create the first impression, which largely coincides with subsequent assessments of taste, aroma, and texture. This phenomenon is particularly important in the case of crisps, where a golden color and the absence of discoloration are key determinants of desired flavor and crispness [17]. The negative correlations between defects (r = −0.71) and discoloration (r = −0.67) and quality assessment are fully consistent with studies by other authors. In the case of crisps, dark spots and uneven color are a direct result of excessive Maillard reaction and may indicate an increased content of reducing sugars in the raw material. Studies by Zgórska and Sowa-Niedziałkowska [43] and Pszczółkowski et al. [16] showed that dark-fried products are rated lower by consumers due to their bitter aftertaste and unappetizing appearance. Moisture stains (r = −0.54) also negatively correlate with quality, as they suggest that the product is not fried or stored properly, which affects its crispiness and stability [16,17].

4.8.1. Focus on Efficiency and Interpretability

PCA analysis (Figure 8) proved highly effective for dimensionality reduction, with just the first three Principal Components (PC1–PC3) explaining over 80% of the total variance in the original dataset. Interpretation of the PC1 and PC2 loadings revealed that PC1 can be identified as ‘Process Efficiency’ (strongly correlated with variables Y1–Y4), while PC2 represents ‘Input Conditions’ (mainly related to variables X1–X6). The two-dimensional biplot clearly visualizes the strong correlation between ultrasonic treatments and favorable tuber quality traits, confirming the effectiveness of this pretreatment in modulating potato metabolism. PCA provided statistical confirmation that the variability in processing outcomes (e.g., chip color and sugar content) can be largely condensed and explained by the combination of agronomic variables and sonication parameters.

4.8.2. Study Identified a Particularly Problematic Relationship

A strong positive correlation (r = 0.67) existed between the occurrence of defects/discolorations and the presence of moisture spots. This interdependence suggests these quality issues frequently co-occur during production, presenting a compounded challenge for manufacturers.

Technical Implications: The correlation patterns indicate that color measurement could serve as a reliable proxy for overall quality assessment. Reducing sugar content in raw materials significantly impacts the final product’s appearance. Moisture control is crucial for minimizing multiple quality defects simultaneously. These findings provide quantitative support for focusing process improvements on raw material selection (reducing sugar content), moisture management during processing, and monitoring color development [8,9,36].

The strength of these correlations (all statistically significant at p < 0.05) underscores their importance in industrial quality control systems. Our research shows that the applied technologies had no significant effect on the levels of soluble and reducing sugars, which is consistent with the reports. The research of these authors shows that the comparison of conventional cultivation with integrated and ecological cultivation did not bring a positive conclusion about the dominance of any of them in relation to the content of reducing sugars. Also, the method of fertilization, with and without the use of manure, had no significant effect on their content. For the same potato variety, the level of reducing sugars was five times higher in tubers harvested in July compared to those harvested at the end of August. The results of the research of most authors [14,39,40,42,54,55,56] confirm unanimously that the earliness of varieties and the degree of maturity of tubers are of significant importance for the content of reducing sugars. Early varieties and those harvested at the stage of incomplete maturity have a higher level of reducing sugars at harvest compared to medium-late varieties and those harvested at the stage of full maturity [42,43]. Our own research, in which eight edible varieties from different earliness groups were tested, confirms this view; a similar relationship was observed in the research by Zgórska and Grudzińska [46]. The research of these authors emphasizes the relationship that the content of reducing sugars decreases with the increase in starch content in potato tubers. Very early and early varieties usually contain lower starch content than medium-late and late varieties. Our own research proves that the use of ultrasound contributed to obtaining chips with a nicer color by 9.5% compared to chips produced from tubers obtained from the control object. Kutlu [3] reports that the effect of ultrasonic applications on food colors is quite positive, and that proper determination of the conditions of ultrasound application is very effective for preserving the color of food. Moreover, these researchers state that minimizing energy and power consumption while simultaneously preserving the color of food is one of the directions of the processing industry. The conducted studies showed that the potato varieties studied differed significantly in terms of the content of total sugars and reduced sugars in tubers and contained, on average, 1.05% total sugars and 0.53% reducing sugars in fresh weight (Table 2). Zgórska and Czerko [12,13] and Murniece et al. [17] showed that the content of reducing sugars is a feature directly related to the genotype of a given variety. In our study of eight potato varieties, the content of reducing monosaccharides ranged from 0.3 g·kg⁻¹ to 3.8 g·kg⁻¹ (Table 3).

According to [9,13,52], the content of total sugars above 10 g·kg⁻¹ in the fresh weight of tubers (glucose + fructose + sucrose) affects the taste of boiled and fried tubers. These authors showed that the increase in saccharide content in tubers does not affect the appearance of their sweet aftertaste; however, tubers of very early and early varieties did not meet the criteria set for raw materials intended for the production of French fries and chips. The results obtained are consistent with the data presented by Zarzecka et al. [45] and Pedrosa et al. [52]. These authors showed that extremely high sugar contents are caused by atmospheric conditions. During wet and cold periods, sugar content in tubers was significantly higher than under optimal conditions. Different results were obtained by Zgórska & Grudzińska [46]. These authors proved that the content of reducing sugars in mature and immature tubers is similar and amounts to an average of 0.2 g·kg⁻¹ of fresh tuber mass. The lack of full maturity of tubers, manifested by high activity of invertase, which hydrolyzes sucrose to glucose and fructose, may indicate that even tubers characterized by harvest maturity, with properly developed skin and debarking of the stolon attachment, come from over-fertilized objects. According to Zgórska & Czerko [12], in tubers intended for processing and stored at a temperature of 6–8 °C, due to intensive respiration, the accumulation of sugars is small; however, the consequences of higher temperatures are intensive physiological processes and premature aging of tubers [54,55]. The limiting temperature at which increased accumulation of glucose and fructose occurs is 7 °C. The reduction in reducing monosaccharides is the result of a “compromise” between low and high storage temperatures. At low temperatures, the development of pathogens is minimized, and metabolic processes are strongly inhibited, while at higher temperatures, the tubers metabolize intensively, but at the same time, they are characterized by a low level of sugars [44]. Zgórska and Sowa-Niedziałkowska [43] and Zarzecka et al. [45] showed that the content of reducing sugars in potato tubers intended for French fries should not exceed optimally 2.5 g·kg⁻¹ (maximum 5 g·kg⁻¹ of dry matter), and for chips, optimally 1.5 g·kg⁻¹ (maximum 2.5 g) of fresh mass. In turn, the content of total sugars should not exceed 10 g·kg⁻¹ of fresh mass in both cases. [44,53,54]. Too high sugar content is undesirable because sugars caramelize during frying, which gives the products a brown color and a bitter taste [54].

4.8.3. Mechanisms of Ultrasound Action and Genetic Variation

Integrating Pre-Sowing Ultrasound with Composition Control: Our key finding—a significant reduction in reducing sugars in tubers from sonicated seed potatoes—provides fundamental confirmation of the effectiveness of pre-sowing ultrasound technology. This result distinguishes our method from conventional ultrasound applications, which typically focus on post-harvest processing, e.g., to inactivate enzymes or improve ultrafiltration [3,6].

Rather than modifying the finished tuber, we demonstrated that brief exposure to low-frequency ultrasound (LUS) during the pre-planting phase acts as a metabolic stimulant. Acoustic cavitation and micro-streaming, induced by ultrasonic wave propagation, likely modulate the activity of enzymes (such as invertase or amylase) or influence the early expression of genes [6] responsible for the synthesis/degradation of sucrose, the basis for reducing sugars (glucose and fructose). As a result, a metabolic change is programmed already at the germination stage, leading to a reduction in sugars in the final yield.

Impact of LUS on Tissue Properties and Chip Quality: The observed improvements in physical parameters of chips, such as lighter color, optimal moisture content, and lower fat absorption, are a direct consequence of this metabolic and physical modulation.

Color and Sugars: A key correlation confirmed in our study is the strong negative relationship between reducing sugars and chip color. Since ultrasound effectively reduced sugar levels, the intensity of the Maillard reaction during frying was reduced, which directly translated into the desired lighter color of the product. Fat and Texture: Our results, indicating lower fat content and optimal moisture content in chips from sonicated tubers (Table 6), suggest that LUS also affects the cellular integrity or surface microporosity of tuber tissue. The short, controlled cavitation effect could have improved cell wall integrity or accelerated the formation of a dry, sealing layer during frying [40], effectively hindering oil penetration while accelerating water evaporation. Shorter exposure to high temperatures allows for better control of browning reactions (including the Maillard reaction), thus helping maintain a more vibrant and attractive color [40].

There is also a synergistic effect: A study by Lu et al. [40] suggests that combining ultrasound with acetic acid soaking not only improved fat reduction but also resulted in a more vibrant and attractive color, indicating a synergistic effect of ultrasound on the chemical and physical processes occurring during frying. Referring to this mechanism, our innovative study focuses on modifying metabolism before planting, i.e., at the very beginning of growth. Our research demonstrates that the use of ultrasound before potato planting significantly reduces the content of reducing sugars in tubers intended for chip production. This is a pre-sowing effect that influences the plant’s subsequent metabolism.

Analysis of Varietal Response and Raw Material Composition: Differences in the response of individual varieties to sonication are crucial for the practical application of technology. We demonstrated that the effects of sugar reduction and color improvement were not uniform across all eight tested varieties, consistent with their diverse genetic backgrounds. The Role of Starch: Varieties characterized by higher starch content and a more compact flesh structure (e.g., ‘Tajfun’ and ‘Syrena’) may have demonstrated different ultrasonic wave penetration efficiency compared to varieties with lower starch content and a looser structure. Variety in flesh density likely influences the efficiency of acoustic wave propagation and the intensity of microcavitation within the tuber.

Conclusions for Variety Selection: Our research indicates that optimal variety selection for this innovative treatment requires consideration of genotypic susceptibility to metabolic modulation. This research provides the basis for the development of a variety selection protocol for the crisp industry, indicating which varieties, in terms of their original starch and sugar content, benefit most from processing quality after LUS pre-sowing.

4.9. Directions and Future Trends in Ultrasound Applications in Food Processing

Ultrasound technology is increasingly being viewed as an eco-friendly and effective alternative to traditional methods in the food industry. Key trends and developments include the following:

Increasing Process Efficiency and Quality: Ultrasound is used to intensify processes such as drying, blanching, and frying, reducing processing time and increasing efficiency. This technology also allows for improved sensory properties, such as texture and color (e.g., in chips), and can extend product shelf life [48,58,59,60].

Minimizing Chemical Use: Ultrasound can effectively inactivate enzymes responsible for undesirable changes (e.g., darkening of fruits and vegetables) and aid in food preservation, reducing the use of chemical additives and preservatives [36,56].

Structure Modification and Extraction: Ultrasound is used to modify the structure of biopolymers (e.g., starch), which can alter the textural properties of products. Furthermore, this technology facilitates the extraction of valuable compounds (e.g., antioxidants and essential oils) from plant materials, which is useful in the production of supplements and functional foods [51,57].

Mechanistic Insight Needed: To fully understand and confirm the mechanisms behind observed reductions in oil and acrylamide absorption and texture improvements, structural analyses (SEM), chemical analyses (FTIR), and instrumental validation of sensory attributes (colorimetry and texture analysis) are necessary. This provides a starting point for further work: This research will form the foundation for our next scientific work, focused on precisely correlating sonication-induced microstructural and molecular changes with final product quality.

Sustainability: The introduction of ultrasound is consistent with the concepts of “green chemistry” and sustainable development. It enables reduced water and energy consumption and generates less waste, which is a response to growing consumer expectations and environmental regulations [55,56].

Quantification of acrylamide: Future research will require the determination and quantification of acrylamide in finished chips derived from sonicated tubers using precise analytical techniques (e.g., LC–MS/MS). This will ultimately confirm the hypothesis that the reduction in reducing sugars achieved with ultrasound directly translates into a reduction in acrylamide content, which is crucial for food safety and the commercial relevance of technology.

Integration and Automation: The future of ultrasound in the food industry lies in its integration with other technologies (e.g., membrane processes) and the development of automated, continuous processing systems on an industrial scale [57,58].

4.10. Limitations of Ultrasound Technology

While ultrasound technology offers numerous advantages in food processing, it also has several limitations and challenges that need to be considered. These are:

Limited Penetration: High-frequency ultrasound waves cannot penetrates deep into dense or complex foods, restricting their effectiveness to the surface [5,8,55,58].

Inconsistent Treatment: The intensity of ultrasound waves weakens as they travel, leading to uneven processing within large volumes of food [11,58].

Food Matrix Dependency: The effectiveness of technology changes based on the specific properties of the food, requiring unique settings for each product [55].

Energy and Cost: Scaling up for industrial use can be energy-intensive and requires significant initial investment in equipment [8,10].

Resistance: Some resilient bacteria and enzymes are not fully inactivated by ultrasound treatment alone, often requiring its combination with other methods like heat or pressure [6,9,14].

Potential for Damage: High-intensity ultrasound can negatively affect food quality by causing texture degradation, lipid oxidation, or alteration in flavors [1,2,4,8,10].

Monitoring Challenges: It is difficult to monitor and control the process in real-time within complex food systems, making it hard to ensure consistent results [10,55,58].

Lack of Standardization: The absence of standardized protocols and guidelines hinders its widespread adoption and regulatory approval [54,55,59,60].

Limited Understanding: The exact mechanisms of how ultrasound interacts with different food components are not fully understood, which makes predicting and optimizing their effects challenging. Addressing these limitations through ongoing research and development is crucial for broader and more effective application of ultrasound technology in the food industry.

“A detailed cost analysis of implementing ultrasound technology on an industrial scale (including energy costs, equipment depreciation, and ROI comparison) is a complex issue that will be discussed in a future dedicated publication after conducting extended pilot studies.”

5. Conclusions

• Main Conclusions (Key Achievements)

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  Ultrasonic treatment of planting tubers (pre-sowing) is an effective, non-chemical method for significantly improving the quality of processing material. This technology leads to a significant reduction in the content of reducing sugars in the final yield, which directly translates into the production of chips with a lighter color and better sensory parameters. The use of ultrasound is a key element of the strategy aimed at reducing the use of chemical additives in processing.

• Specific Application Conclusions (Recommendations for Industry)

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  Application Conclusions (Recommendations)

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  Variety Selection: We maintain our recommendation for the ‘Tajfun’ and ‘Syrena’ varieties.

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  Cost-Effectiveness: Ultrasound treatment is most cost-effective and effective for “borderline” varieties (‘Vineta’ and ‘Satina’) and in years with unfavorable weather conditions, where the risk of high sugar levels is greatest.

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  Environmental Factors: The dominant role of environmental factors requires adapting the technology to local agronomic conditions.

• Limitations and Future Research Directions (Current Limitations)

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  Scalability and Economics: Lack of comprehensive economic analysis (ROI) and verification of industrial scalability (ultrasonic field uniformity and power density control in large reactors).

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  Biochemical Mechanism: The explanation of the sugar reduction mechanism is inferential (enzymatic modulation suggested). Direct molecular verification is required (e.g., proteomic/transcriptomic studies).

• Future Research (Action Plan)

  −

  Economic Analysis and Optimization: A detailed ROI analysis on an industrial scale and optimization of process parameters (ultrasonic frequency and power density) for individual varieties are necessary.

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  Predictive Modeling: Development of predictive models integrating agronomic conditions, genotype, and sonication parameters to optimize chip quality.

Broader Validation: Conduct research under diverse soil, climatic, and regional conditions. Breeding Support: Analysis of the response of varieties to sonication as a tool for early selection of genotypes best suited for chip processing.