1. Introduction
Peach is a popular fruit appreciated by consumers due to its eating quality. It is rich in a variety of vitamins and minerals, including carbohydrates, organic acids, pigments, phenolics, vitamins, volatiles, antioxidants, and small amounts of proteins and lipids [
1,
2]. Quality evaluation of peaches is important for processing, inventory control, and marketing. Physical and chemical quality detection methods (detection of firmness, soluble solids concentration, titratable acidity, etc.) accurately determine the quality of the fruit [
3,
4]. However, these detection methods are time-consuming and require special experimental equipment and conditions, making them impractical during actual production [
3].
In many cases, practical peach quality evaluation consists of appearance screening by operators, which is influenced by subjective factors and has a low efficiency and large errors [
5]. As labor costs increase, labor-intensive evaluation and grading constitutes a major expense for fresh and processed peach postharvest management [
6]. Therefore, they are gradually being replaced by automated evaluation systems based on machine vision, image processing technology, and other emerging detection technologies. These approaches, which include measurement of dielectric properties and hyperspectral imaging, have the potential to improve processing efficiency, reduce costs, and minimize waste [
7,
8].
For example, Zhang et al. [
9] designed a 13-layer convolutional neural network (CNN) for fruit category identification with three types of data augmentation. Rajkumar et al. [
10] studied banana quality and maturity using hyperspectral imaging in the visible and near-infrared (400–1000 nm) regions, and Keresztes et al. [
11] developed a real-time pixel-based early apple bruise detection system based on hyperspectral imaging (HIS) in the shortwave infrared (SWIR) range. However, the appearance of fruit is often affected by ripening agents and experimental conditions (e.g., simple glossiness, image background), and quality differences cannot be fully captured by imaging [
11,
12]. Soltani et al. [
13] proposed a rapid and non-destructive method for investigating the correlation between the dielectric constant and quality parameters of banana fruit. Ma et al. [
14] investigated changes in the dielectric properties of Fuji apples with red-dot disease that were stored at constant temperature, and Du et al. [
15] reported that 13 dielectric properties of peaches showed regular changes with increasing frequency. However, further research and more cases are required to apply emerging detection technologies to quality evaluation of fresh fruit.
It is difficult to accurately assess the quality of an entire batch of fruit based on single evaluation indicators and limited samples; such assessments are affected by the experimental environment and the characteristics of the individual fruit sampled [
16,
17]. Fruit quality evaluation methods combined with multiple detection technologies, therefore, receive significant attention [
18,
19]. For example, Das et al. [
20] described a platform for evaluation of honey quality based on electrical impedance spectroscopy (EIS) and Fourier-transform mid-infrared spectroscopy (FT-MIR), which was used to detect the presence of sucrose as an adulterant in honey varieties from different floral origins. Lubinska-Szczygel et al. [
21] used an electronic nose based on ultrafast gas chromatography and gas chromatography with mass spectrometry to analyze the quality of three citrus fruits.
Currently, fruit quality assessment still requires more convenient and effective evaluation methods [
22]. In this work, a rapid and simple measurement of electrical properties was used to predict related differences in the quality of fresh peaches. In addition, principal component analysis (PCA) was used to develop a comprehensive method and model for effectively evaluating and grading peach quality. This model can be used to guide consumers’ choices when buying fresh peaches. It is hoped that the approach and findings of this study will promote further research in the field of fresh fruit quality evaluation.
2. Materials and Methods
2.1. Experimental Materials and Instruments
Instruments used in the present work included a handheld LCR (Inductance, capacitance, and resistance) meter (VICTOR 4082, Shenzhen, China, frequency range: 0–100 kHz, target indicators: impedance and phase angle), a Color Tec-PCM Plus 30 mm Benchtop Colorimeter (Color Tec Associates, Clinton, NJ, USA, target indicators: L, a, b, C, and H), a refractometer (PAL-1, ATAGO, Japan, measurement range: brix 0.0%–53.0%, measurement accuracy: brix ±0.2%), a penetrometer (FM200, PCE, Germany) fitted with a 7.9-mm-diameter plunger, a titrator (DCB5000, BOECO, Germany), an electronic scale (OHAUS Adventurer AX2202, NJ, USA), and a digital caliper.
Commercial fresh peaches (
Prunus persica (L.) Batsch ‘Spring Belle’) obtained from a supermarket in Zagreb were used as the experimental sample. Two hundred peaches were placed in cold storage at 0 °C and numbered for use in the experiment. The measurement of nine quality indicators was completed within 48 h and was performed in sequential steps: color, shape index, volume, mass, dielectric properties, firmness, soluble solid concentration (SSC), and titratable acid (TA) [
23,
24]. Two more quality indicators (density and sugar–acid ratio) were acquired by calculation.
2.2. Determination of Indicators
The color of peaches (L, a, b, C, H) was assessed according to the International Commission on Illumination (CIE) Delta E 2000 (CIEDE2000) color space using a colorimeter, and test times were less than three minutes. Test points were selected from most red-colored and the most light-colored parts of the fruit surface, and average values were used for data analysis. The CIEDE2000 formula was developed by members of the CIE Technical Committee, providing an improved procedure for the computation of industrial color differences [
25,
26]. The formula is as follows:
where ΔE is the change in color, RT is a hue rotation term, ΔL, ΔC, and ΔH are the compensation differences for neutral colors (primed values;
L,
C,
H),
SL is the compensation for lightness,
SC is the compensation for chroma,
SH is the compensation for hue, and
KL,
KC, and
KH are constants and usually in unity.
Based on the proportion of red area on the surface of the fresh peach, the operator divided the samples into five appearance quality grades. Consumers in the fruit market always choose fresh peaches with more red areas [
27].
Peaches were treated as a sphere for volume measurement, and diameter was estimated based on the average of height and width, as shown in
Figure 1a. Peach mass was measured using an electronic scale with an error range of 0.01 g. The shape index (height/width) and density (mass/volume) were acquired by calculation.
Dielectric properties were measured using published procedures with a level of 500 mV, a test time of 1 min, a frequency of 10 kHz, and a bias voltage of 0 mV [
28,
29]. The target experimental indicators were impedance (
Zs) and phase angle (
θ). Measurement range was set to automatic mode, and test speed was set to fast mode [
14,
30]. The experimental device is shown in
Figure 2.
The contact probe was designed and manufactured using copper as a conductive material [
15]. The probe was completely inserted into the pulp of the peach. After one minute, the values on the instrument display were paused and recorded. The probe was wiped with alcohol on cotton before measuring the next sample. Avoiding areas damaged by the contact probe, four planes (with obvious pulp) were sliced from four quadrants of peaches (excluding the bottom and top) for firmness measurements, as shown in
Figure 1b. Fruit firmness was determined at four equatorial positions on each fruit at 90° [
22] after skin was carefully removed.
Juice was extracted from the pulp of peaches and used for SSC determination. An additional 5 g of the remaining juice (without pulp) was sampled, and, after adding few drops of bromothymol blue indicator, the titration solution (0.1 mol/L NaOH) was dropped into the bottle until the juice turned from yellow to olive green, as shown in
Figure 1c. The TA content of the juice was determined from the volume of the titrated solution, and the sugar–acid ratio (
SSC/
TA) was acquired by calculation.
2.3. Data processing and Analysis
An outline of data processing and analysis steps is shown in
Figure 3. The 11 indicators had different dimensions, and indicator data were, therefore, normalized prior to analysis. Indicator data from 200 peaches were grouped and normalized using the
Z-score normalization method in SPSS (IBM ver. 25.0), resulting in dimensionless datasets with an average of zero and a standard deviation of one [
31,
32]. Data points outside the range of (−3, 3) were considered to be outliers, and data from 10 samples were excluded to ensure a normal distribution (−3σ, 3σ) of the processed data. Normalized data from 11 indicators measured in 190 peaches were then renumbered for subsequent analyses. Based on correlation analysis, dielectric properties appeared to best characterize and predict values of the other indicators in peaches (see below).
PCA and k-means clustering analysis were used to develop an evaluation and grading method for fresh peach quality. A principal component is a new indicator that cannot be directly measured by experiment. The content of each principal component can be defined by a component score (
Fnj) obtained from the following formula:
where
Zni is the normalized indicator value of sample,
Fnj is the score of the
j-th principal component for the
n-th peach, and
eji is the load of
i-th original indicator in the
j-th principal component. Each indicator has a different weight for evaluating the quality of a sample. The final composite score requires a linear weighted summation. The variance contribution rate can be used as the weight value, representing the extent to which the indicator data reflect the overall data. The comprehensive score can be obtained from the following formula:
where
Wn represents the composite score of
n-th peach, and
nj represents the variance of the
j-th principal component.
4. Conclusions
In this study, the measurement of 11 quality indicators of 200 peaches was completed within 48 h. Quality indicator data were normalized, and outliers were excluded. A BP neural network was constructed in MATLAB to predict the firmness of fresh peaches from their dielectric properties, and the overall fitting ratio of the predicted values and the observed values was 86.9%. PCA results indicated that the cumulative variance explained by the first five principal components was 85%, indicating that the first five components captured most of the information from the 11 original indicators. Based on k-means clustering of normalized data from 11 indicators, 190 peaches were divided to five clusters, containing 24.7%, 19%, 14.2%, 21.6%, and 20.5% of the samples, respectively. The average comprehensive score was calculated for each cluster; fresh peach quality of cluster 1 was the best, and fresh peach quality of cluster 4 was the worst. Poor correspondence between the comprehensive scores and the percentage of red surface area demonstrated that red surface area is not a good basis for picking fresh peaches for consumers.
This study establishes that dielectric properties can be used to predict the firmness of fresh peaches, and it describes an evaluation and grading model based on multiple indicators. The model provides a scientific, objective, and feasible way of studying and classifying the quality of fresh peaches. It provides a reference for the proposed evaluation of quality in other foods and a technical guarantee of food safety to protect the rights of the consumer. This research provides a technical basis for the detection of dielectric properties related to fruit quality, which will be an important focus of future studies. Based on this research, more convenient and effective fruit quality detection methods will be developed, providing technical support for post-harvest evaluation and classification of fruits in future work.