Search Results (59)

Soil moisture sensor-based drip irrigation enables efficient irrigation practices by delivering the required water to plants. However, efficiency must be accompanied by uniform water management and crop growth. This study examined the effect of different irrigation amounts (IAs) per application (5.5, 55, 110, and 165 mL) on the uniformity of substrate volumetric water content (VWC) within an irrigation plot, and the corresponding effect on sweet basil growth uniformity. Sixty-four frequency domain reflectometry sensors monitored the VWC of each 440 mL pot, and drip irrigation was automatically applied at 0.3 m³·m⁻³. The 5.5 mL IA showed the highest water use efficiency; however, it also resulted in considerable non-uniform VWC (coefficient of variation, CV = 0.404). In contrast, the 110 and 165 mL IAs provided better VWC uniformity (CV = 0.073 and 0.075, respectively), suggesting that less frequent, but larger IAs improved VWC uniformity. Despite the differences in VWC uniformity among treatments, the growth and physiological responses were quite similar across the treatments. It was found that supplying 110 mL irrigation water via the soil moisture sensor-based drip irrigation system to sweet basil plants in 440 mL pots is optimal for achieving both water use efficiency and VWC uniformity. Full article

Internet-of-Things (IoT) approaches are continually introducing new sensors into the fields of agriculture and animal welfare. The application of multi-sensor data fusion to these domains remains a complex and open-ended challenge that defies straightforward optimization, often requiring iterative testing and refinement. To respond to this need, we have created a new open-source framework as well as a corresponding Python tool which we call the “Data Fusion Explorer (DFE)”. We demonstrated and evaluated the effectiveness of our proposed framework using four early-stage datasets from diverse disciplines, including animal/environmental tracking, agrarian monitoring, and food quality assessment. This included data across multiple common formats including single, array, and image data, as well as classification or regression and temporal or spatial distributions. We compared various pipeline schemes, such as low-level against mid-level fusion, or the placement of dimensional reduction. Based on their space and time complexities, we then highlighted how these pipelines may be used for different purposes depending on the given problem. As an example, we observed that early feature extraction reduced time and space complexity in agrarian data. Additionally, independent component analysis outperformed principal component analysis slightly in a sweet potato imaging dataset. Lastly, we benchmarked the DFE tool with respect to the Vanilla Python3 packages using our four datasets’ pipelines and observed a significant reduction, usually more than 50%, in coding requirements for users in almost every dataset, suggesting the usefulness of this package for interdisciplinary researchers in the field. Full article

The evaluation of aroma-active profiles in dry-cured hams is crucial for determining quality, flavor, consumer acceptance, and economic value. This study characterized the volatile compounds in five varieties of dry-cured hams using gas chromatography-mass spectrometry-olfactometry (GC-MS-O) and an electronic nose (E-Nose). In total, 78 volatile compounds were identified across five varieties of dry-cured hams. A total of 29 compounds were recognized as aroma-active compounds. Odor description, intensity, and hedonic assessment were employed to evaluate these compounds. Black Hoof Cured Ham and Special-grade Xuan-Zi Ham contained higher levels of favorable compounds such as nonanal, 5-butyldihydro-2(3H)-furanone, and 2,6-dimethylpyrazine, contributing to sweet and popcorn-like notes. In contrast, Fei-Zhong-Wang Ham and Liang-Tou-Wu Ham exhibited higher proportions of off-odor compounds with lower hedonic scores. A principal component analysis clearly separated the five hams based on their aroma-active profiles, and a correlation analysis between E-Nose sensor responses and GC-MS-O data demonstrated a strong discriminatory ability for specific samples. These findings offer valuable insights into the chemical and sensory differentiation of dry-cured hams and provide a scientific basis for quality control, product development, and future improvements in E-Nose sensor design and intelligent aroma assessment. Full article

With the surge in digital farming, real-time quality management of fresh produce has become essential. For apples (Malus domestica Borkh.), consumer demand extends beyond sweetness, texture, and appearance to internal quality factors such as moisture content. Existing non-destructive methods, however, involve costly equipment, complex calibration, and sensitivity to environmental conditions. This study hypothesizes that thermal diffusivity indices derived from surface heating and cooling patterns can accurately predict apple moisture content non-destructively. A total of 823 apples from seven varieties were analyzed using a thermal imaging sensor in a 120-s process comprising 40 s of heating and 80 s of cooling. Key thermal diffusivity indices—minimum, maximum, mean, and max–min values—were extracted and correlated with actual moisture content measured via the drying method. Multiple linear regression and leave-one-out cross-validation confirmed that mean temperature-based models provided the most stable predictions ($R_{CV}^2$ ≥ 0.90 for some varieties). Frame optimization and artificial neural networks further improved prediction accuracy for varieties exhibiting higher variability. The proposed method is cost-effective, requires minimal calibration, and is less affected by surface reflectance, outperforming conventional optical methods (e.g., NIR spectroscopy, hyperspectral imaging), especially regarding robustness against surface reflectance variability and calibration complexity. This offers a practical solution for monitoring apple freshness and quality during sorting and distribution processes, with expanded research on sugar content and acidity expected to accelerate commercialization. Full article

The effect of annealing treatment on an inorganic film for taste sensors has not been fully elucidated. In this study, we developed an inorganic film taste sensor using SnO₂ as a sensitive film and evaluated the effect of annealing treatment on its sensing response to sweet substances. First, we confirmed from XRD patterns that annealing at 600 °C caused a change in crystal orientation. Next, the taste sensor response to acesulfame potassium solution, which is a high-intensity sweetener and an electrolyte, showed a negative response with high concentration dependence. On the other hand, the sensors exhibited a positive response to non-electrolytes such as aspartame and glucose, with the sensor annealed at 600 °C showing a larger response to non-electrolytes compared to the other sensors. In terms of concentration dependence, the response to aspartame was higher, whereas the response to glucose was lower. Also, a reduction in variability was observed after annealing treatment at 150 °C and 300 °C. This phenomenon was clarified by comprehensively investigating various properties. Full article

Improving floral scent quality is an important goal in Malus breeding. However, the inheritance regularity for volatile components of Malus remains unclear. In this study, the floral scent compounds and scent characteristics of five Malus taxa with clearly defined parent–progeny relationships were analyzed by sensory evaluation, an electronic nose, and gas chromatography–mass spectrometry. A total of 51 volatile compounds were identified in five taxa. M. ioensis showed the highest sensory intensity with the maximum total content of compounds (8247.59 ng·g⁻¹ FW·h⁻¹). Compared to its progenies and ‘Lemoinei’, terpenoid compounds in M. ioensis accounted for the largest proportion (40.46%). Most compounds in the progenies were inherited from their maternal parent (60.61–75.00%), and most of them were significantly downregulated by hybridization. However, the content of several compounds in the progenies appeared transgressive, even unique. Progenies and their maternal parents exhibited similar sensory characteristics: earthy/woody, sweet, and rose. The content of characteristic compounds (geranylacetone, 6-methyl-5-hepten-2-one, 2-phenylethanol, α-ionone, β-ionone, decanal, and so on), total content, and the response of sensor W3S positively correlated with scent intensity. The response of sensor W1W correlated significantly and positively with the compound number and the total content. Our findings provided a reference for tracking maternal parents for cultivars and enabled rapid selection of fragrant flower cultivars by electronic nose. Full article

Watermelons are in high demand for their juicy texture and sweetness, which is linked to their soluble solids content (SSC). Traditionally, watermelons have been sold as whole fruits. However, the decline in the mean size of households and the very large size of the fruits, together with high prices, mainly at the beginning of the season, mean that supermarkets now sell them as half fruits. For consumers, it is important to know in advance that the fruits that they are purchasing are of a high quality, based not only on external flesh colour but also on sweetness. Near-infrared spectroscopy (NIRS) and Raman spectroscopy were used for the in situ determination of SSC in half watermelons while simulating supermarket conditions. A handheld linear variable filter (LVF) device and an all-in-one (AIO) Process Raman analyser were used for the NIRS and Raman analysis, respectively. The excellent results obtained—including residual predictive deviation for prediction (RPD_p) values of 3.06 and 2.90 for NIRS and Raman, respectively—showed the viability of NIRS and Raman spectroscopies for the prediction of sweetness in half watermelons. Full article

Currently, lipid/polymer membranes are used in taste sensors to quantify food taste. This research aims to improve sweetness sensors by more selectively detecting uncharged sweetening substances, which have difficulty obtaining a potentiometric response. Lipid/polymer membranes with varying amounts of tetradodecylammonium bromide (TDAB) and 1,2,4-benzene tricarboxylic acid (trimellitic acid) were prepared. The carboxyl groups of trimellitic acid bind metal cations, and the sweetness intensity is estimated by measuring the potential change, as a sensor response, when these cations are complexed with sugars. This research showed that the potential of a sensor using the membrane with enough trimellitic acid in a sucrose solution remained constant, regardless of TDAB amounts, but the potential in the tasteless, so-called reference solution, depended on TDAB. By optimizing the content of TDAB and trimellitic acid, a sensor response of −100 mV was achieved, which is over 20% more sensitive than a previous sensor. This sensor also demonstrated increased selectivity to sweetness, with similar interference from other tastes (saltiness, sourness, umami, and bitterness) compared to previous sensors. As a result, the sensitivity to sweetness was successfully improved. This result contributes to the development of novel sensors, further reducing the burden on humans in quality control and product development. Full article

Various environmental factors not only affect plant growth and physiological responses individually but also interact with each other. To examine the impact of light intensity on the drought responses of sweet basil, plants were subjected to maintenance of two substrate volumetric water contents (VWC) using a sensor-based automated irrigation system under two distinct light intensities. The VWC threshold was set to either a dry (0.2 m³·m⁻³) or sufficiently wet condition (0.6 m³·m⁻³) under low (170 μmol·m⁻²·s⁻¹) or high light intensities (500 μmol·m⁻²·s⁻¹). The growth and physiological responses of sweet basil (Ocimum basilicum L.) were observed over 21 days in the four treatment groups, where the combination of two environmental factors was analyzed. Under high light intensity, sweet basil showed lower F_v/F_m and quantum yield of PSII, compared to that under low light intensity, regardless of drought treatment. Fourteen days after drought treatment under high light intensity, stomatal conductance and the photosynthetic rate significantly reduced. Whereas plants under low light intensity showed similar stomatal conductance and photosynthetic rates regardless of drought treatment. Assessment of shoot and root dry weights revealed that plant growth decline caused by drought was more pronounced under high light intensity than under low light intensity. Thus, sweet basil showed significant declines in growth and physiological responses owing to drought only under high light intensity; no significant changes were observed under low light intensity. Full article

This study analyzed the capability of aerial RGB (red-green-blue) and hyperspectral-derived vegetation indices to assess the response of sweet maize (Zea mays var. saccharata L.) to different water and nitrogen inputs. A field experiment was carried out during 2020 by using both remote RGB images and ground hyperspectral sensor data. Physiological parameters (i.e., leaf area index, relative water content, leaf chlorophyll content index, and gas exchange parameters) were measured. Correlation and multivariate data analysis (principal component analysis and stepwise linear regression) were performed to assess the strength of the relationships between eco-physiological measured variables and both RGB indices and hyperspectral data. The results revealed that the red-edge indices including CI_red-edge, NDRE and DD were the best predictors of the maize physiological traits. In addition, stepwise linear regression highlighted the importance of both WI and WI:NDVI for prediction of relative water content and crop temperature. Among the RGB indices, the green-area index showed a significant contribution in the prediction of leaf area index, stomatal conductance, leaf transpiration and relative water content. Moreover, the coefficients of correlation between studied crop variables and GGA, NDLuv and NDLab were higher than with the hyperspectral indices measured at the ground level. The findings confirmed the capacity of selected RGB and hyperspectral indices to evaluate the water and nitrogen status of sweet maize and provided opportunity to expand experimentation on other crops, diverse pedo-climatic conditions and management practices. Hence, the aerially collected RGB vegetation indices might represent a cost-effective solution for crop status assessment. Full article

Apples are widely cultivated in the Republic of Korea and are preferred by consumers for their sweetness. Soluble solid content (SSC) is measured non-destructively using near-infrared (NIR) spectroscopy; however, the SSC measurement error increases with the change in apple size since the distance between the light source and the near-infrared sensor is fixed. In this study, spectral characteristics caused by the differences in apple size were investigated. An optimal SSC prediction model applying partial least squares regression (PLSR) to three measurement conditions based on apple size was developed. The three optimal measurement conditions under which the Vis/NIR spectrum is less affected by six apple size levels (Levels I–VI) were selected. The distance from the apple center to the light source and that to the sensor were 125 and 75 mm (Distance 1), 123 and 75 mm (Distance 2), and 135 and 80 mm (Distance 3). The PLSR model applying multiplicative scatter correction pretreatment under Distance 3 measurement conditions showed the best performance for Level IV-sized apples (${\mathrm{R}}_{\mathrm{p}\mathrm{r}\mathrm{e}}^2$ = 0.91, RMSEP = 0.508 °Brix). This study shows the possibility of improving the SSC prediction performance of apples by adjusting the distance between the light source and the NIR sensor according to fruit size. Full article

The flavour and mouthfeel of peaches are crucial qualities of peach germplasm resources that significantly influence consumer preferences. In this study, we utilized 212 peach germplasm resources from the Nanjing Peach Resource Repository, National Fruit Germplasm facility, Jiangsu Academy of Agricultural Sciences as materials for sensory analysis, electronic nose analysis, and composition analysis via high-performance liquid chromatography (HPLC). In the sensory analysis, we divided 212 peach germplasms into three clusters based on hierarchical cluster analysis (d = 5). No.27, No.151, and No.46 emerged as the most representative of these clusters. The electronic nose was used to conduct an evaluation of the aroma profiles of the 212 peach germplasms, revealing that the primary distinguishing factors of peach aroma can be attributed to three sensors: W1S (methane), W1W (terpenes and organosulfur compounds), and W5S (hydrocarbons and aromatic compounds). The primary differences in the aromatic substances were characterized by sensors W2W (aromatic compounds, sulphur, and chlorine compounds) and W1C (aromatic benzene). The HPLC analysis indicated that the persistence of peach sensory characteristics was positively correlated with acids and sourness and negatively correlated with sweetness and the ratio of sugar to acids. The overall impression of the 212 peach germplasms revealed a negative correlation with acids, while a positive correlation was observed between the overall impression and the ratio of sugar to acids. Therefore, this study substantially contributes to the preliminary screening of the analysed specific characteristics of peach germplasms such as No.27, No.46, No.151, and No.211. These selections may provide valuable information for the potential creation of superior germplasm resources. Full article

Currently, taste sensors utilizing lipid polymer membranes are utilized to assess the taste of food products quantitatively. During this process, it is crucial to identify and quantify basic tastes, e.g., sourness and sweetness, while ensuring that there is no response to tasteless substances. For instance, suppression of responses to anions, like tasteless NO₃⁻ ions contained in vegetables, is essential. However, systematic electrochemical investigations have not been made to achieve this goal. In this study, we fabricated three positively charged lipid polymer membranes containing oleylamine (OAm), trioctylemethylammonium chloride (TOMACl), or tetradodecylammonium bromide (TDAB) as lipids, and sensors that consist of these membranes to investigate the potential change characteristics of these sensors in solutions containing different anions (F⁻, Cl⁻, Br⁻, NO₃⁻, I⁻). The ability of each anion solution to reduce the positive charge on membranes and shift the membrane potential in the negative direction was in the following order: I⁻ > NO₃⁻ > Br⁻ > Cl⁻ > F⁻. This order well reflected the order of size of the hydrated ions, related to their hydration energy. Additionally, the OAm sensor displayed low ion selectivity, whereas the TOMACl and TDAB sensors showed high ion selectivity related to the OAm sensor. Such features in ion selectivity are suggested to be due to the variation in positive charge with the pH of the environment and packing density of the OAm molecule in the case of the OAm sensor and due to the strong and constant positive charge created by complete ionization of lipids in the case of TOMACl and TDAB sensors. Furthermore, it was revealed that the ion selectivity varies by changing the lipid concentration in each membrane. These results contribute to developing sensor membranes that respond to different anion species selectively and creating taste sensors capable of suppressing responses to tasteless anions. Full article

Biomaterials offer great potential for enhancing the performance of humidity sensors, which play a critical role in controlling moisture levels across different applications. By utilizing environmentally friendly, sustainable, and cost-effective biomaterials, we can improve the manufacturing process of these sensors while reducing our environmental impact. In this study, we present a high-performance humidity sensor that utilizes purple sweet potato peel (PSPP) as both the substrate and sensing layer. The PSPP is chosen for its polar hydrophilic functional groups, as well as its environmentally friendly nature, sustainability, and cost-effectiveness. Remarkably, this humidity sensor does not require an external substrate. It exhibits a wide detection range of 0 to 85% relative humidity at various operating frequencies (100 Hz, 1 kHz, and 10 kHz) in ambient temperature, demonstrating its effectiveness in responding to different humidity levels. The sensor achieves a high sensitivity value of 183.23 pF/%RH and minimal hysteresis of only 5% at 10 kHz under ambient conditions. It also boasts rapid response and recovery times of 1 and 2 s, respectively, making it suitable for use in high-end electronic devices. Moreover, the sensor’s applications extend beyond environmental monitoring. It has proven effective in monitoring mouth and nasal breathing, indicating its potential for respiratory monitoring and noncontact proximity response. These findings suggest that sweet potato peel material holds great promise as a highly stable, non-toxic, biodegradable, cost-effective, and environmentally friendly option for various domains, including healthcare monitoring. Full article

An automatic determination of grape must ingredients during the harvesting process would support cellar logistics and enables an early termination of the harvest if quality parameters are not met. One of the most important quality-determining characteristics of grape must is its sugar and acid content. Among others, the sugars in particular determine the quality of the must and wine. Chiefly in wine cooperatives, in which a third of all German winegrowers are organized, these quality characteristics serve as the basis for payment. They are acquired upon delivery at the cellar of the cooperative or the winery and result in the acceptance or rejection of grapes and must. The whole process is very time-consuming and expensive, and sometimes grapes that do not meet the quality requirements for sweetness, acidity, or healthiness are destroyed or not used at all, which leads to economic loss. Near-infrared spectroscopy is now a widely used technique to detect a wide variety of ingredients in biological samples. In this study, a miniaturized semi-automated prototype apparatus with a near-infrared sensor and a flow cell was used to acquire spectra (1100 nm to 1350 nm) of grape must at defined temperatures. Data of must samples from four different red and white Vitis vinifera (L.) varieties were recorded throughout the whole growing season of 2021 in Rhineland Palatinate, Germany. Each sample consisted of 100 randomly sampled berries from the entire vineyard. The contents of the main sugars (glucose and fructose) and acids (malic acid and tartaric acid) were determined with high-performance liquid chromatography. Chemometric methods, using partial least-square regression and leave-one-out cross-validation, provided good estimates of both sugars (RMSEP = 6.06 g/L, R² = 89.26%), as well as malic acid (RMSEP = 1.22 g/L, R² = 91.10%). The coefficient of determination (R²) was comparable for glucose and fructose with 89.45% compared to 89.08%, respectively. Although tartaric acid was predictable for only two of the four varieties using near-infrared spectroscopy, calibration and validation for malic acid were accurate for all varieties in an equal extent like the sugars. These high prediction accuracies for the main quality determining grape must ingredients using this miniaturized prototype apparatus might enable an installation on a grape harvester in the future. Full article