Accurate non-destructive prediction of peach fruit internal quality and physiological maturity with a single scan using near infrared spectroscopy

Quantitative analysis of soil potassium by near-infrared (NIR) spectroscopy combined with a three-step progressive hybrid variable selection strategy

Soil potassium is a crucial nutrient element necessary for crop growth, and its efficient measurement has become essential for developing rational fertilization plans and optimizing crop growth benefits. At present, data mining technology based on near-infrared (NIR) spectroscopy analysis has proven to be a powerful tool for real-time monitoring of soil potassium content. However, as technology and instruments improve, the curse of the dimensionality problem also increases accordingly. Therefore, it is urgent to develop efficient variable selection methods suitable for NIR spectroscopy analysis techniques. In this study, we proposed a three-step progressive hybrid variable selection strategy, which fully leveraged the respective strengths of several high-performance variable selection methods. By sequentially equipping synergy interval partial least squares (SiPLS), the random forest variable importance measurement (RF(VIM)), and the improved mean impact value algorithm (IMIV) into a fusion framework, a soil important potassium variable selection method was proposed, termed as SiPLS-RF(VIM)-IMIV. Finally, the optimized variables were fitted into a partial least squares (PLS) model. Experimental results demonstrated that the PLS model embedded with the hybrid strategy effectively improved the prediction performance while reducing the model complexity. The RMSET and RT on the test set were 0.01181% and 0.88246, respectively, better than the RMSET and RT of the full spectrum PLS, SiPLS, and SiPLS-RF(VIM) methods. This study demonstrated that the hybrid strategy established based on the combination of NIR spectroscopy data and the SiPLS-RF(VIM)-IMIV method could quantitatively analyze soil potassium content levels and potentially solve other issues of data-driven soil dynamic monitoring.

Early Detection of Slight Bruises in Yellow Peaches (Amygdalus persica) Using Multispectral Structured-Illumination Reflectance Imaging and an Improved Ostu Method

Assessing the internal quality of fruits is crucial in food chemistry and quality control, and bruises on peaches can affect their edible value and storage life. However, the early detection of slight bruises in yellow peaches is a major challenge, as the symptoms of slight bruises are difficult to distinguish. Herein, this study aims to develop a more simple and efficient structured-illumination reflectance imaging system (SIRI) and algorithms for the early nondestructive detection of slight bruises in yellow peaches. Pattern images of samples were acquired at spatial frequencies of 0.05, 0.10, 0.15, and 0.20 cycle mm-1 and wavelengths of 700, 750, and 800 nm using a laboratory-built multispectral structured-illumination reflectance imaging system (M-SIRI), and the direct component (DC) and alternating component (AC) images were obtained by image demodulation. A spatial frequency of 0.10 cycle mm-1 and wavelength of 700 nm were determined to be optimal for acquiring pattern images based on the analysis of the pixel intensity curve of the AC image; then, the pattern images of all yellow peaches samples were obtained. The ratio image (RT) between the AC image and the DC image significantly enhances bruise features. An improved Otsu algorithm is proposed to improve the robustness and accuracy of the Otsu algorithm against dark spot noise in AC and RT images. As a comparison, the global thresholding method and the Otsu method were also applied to the segmentation of the bruised region in all samples. The results indicate that the I-Otsu algorithm has the best segmentation performance for RT images, with an overall detection accuracy of 96%. This study demonstrates that M-SIRI technology combined with the I-Otsu algorithms has considerable potential in non-destructive detection of early bruises in yellow peaches.

Farm to fork applications: how vibrational spectroscopy can be used along the whole value chain?

Vibrational spectroscopy is a nondestructive analysis technique that depends on the periodic variations in dipole moments and polarizabilities resulting from the molecular vibrations of molecules/atoms. These methods have important advantages over conventional analytical techniques, including (a) their simplicity in terms of implementation and operation, (b) their adaptability to on-line and on-farm applications, (c) making measurement in a few minutes, and (d) the absence of dangerous solvents throughout sample preparation or measurement. Food safety is a concept that requires the assurance that food is free from any physical, chemical, or biological hazards at all stages, from farm to fork. Continuous monitoring should be provided in order to guarantee the safety of the food. Regarding their advantages, vibrational spectroscopic methods, such as Fourier-transform infrared (FTIR), near-infrared (NIR), and Raman spectroscopy, are considered reliable and rapid techniques to track food safety- and food authenticity-related issues throughout the food chain. Furthermore, coupling spectral data with chemometric approaches also enables the discrimination of samples with different kinds of food safety-related hazards. This review deals with the recent application of vibrational spectroscopic techniques to monitor various hazards related to various foods, including crops, fruits, vegetables, milk, dairy products, meat, seafood, and poultry, throughout harvesting, transportation, processing, distribution, and storage.

Application of nondestructive techniques for peach (Prunus persica) quality inspection: A review

Peaches are highly valued for their rich nutritional content. Traditional fruit quality accessing methods (i.e., manual squeezing the fruit for firmness) are both subjective and destructive, which tend to diminish the integrity of fruit samples, consequently undermining their market value. Compared to traditional detection methods, nondestructive technology offers efficient and noninvasive solutions for rapidly and accurately assessing internal external quality of peaches. This can significantly enhance product classification and quality assurance while reducing the need for extensive human resources and minimizing potential physical damage to peaches. This review provided a comprehensive overview of nondestructive techniques for peach quality evaluation, including visible/near-infrared spectroscopy, machine vision technology, hyperspectral imaging, dielectric and optical properties, fluorescence spectroscopy, electronic nose/tongue, and acoustic vibration methods. It also evaluates the effectiveness of each technique in assessing internal quality, maturity, and disease detection of peaches. The advantages and limitations of each method were also summarized. This study focuses specifically on peaches and encompasses all existing nondestructive testing methods, providing valuable insights and references for future studies in the field of peach quality analysis using nondestructive testing methods.

Manipulator with Integrated Flexible Tactile Sensing Arrays for Kiwifruit Ripeness and Size Classification

Fruit grading for ripeness and size is an essential process in the supply chain. Incorrect grading can easily lead to spoiled and degraded fruits entering the market, reducing consumers' confidence in purchasing. At the same time, it is easy to cause the fruit supply chain to reduce profits, unreasonable resource allocation, and related practitioners' income. The current mainstream machine vision grading and manual grading in the production line have dilemmas such as susceptibility to environmental interference, inconsistent grading standards, high cost, and labor shortage. To overcome these problems, this study proposes an integrated flexible tactile sensing array (3 × 4) manipulator for efficient, stable, low-cost, and accurate ripeness and size grading of kiwifruit. The flexible sensing manipulator grasps the kiwifruit, detects the hardness of the kiwifruit by relying on tactile sensing, and determines the ripeness level based on the hardness. The size of the kiwifruit is also differentiated according to whether there is a significant change in the resistance of the topmost sensing unit of the flexible pressure sensor array. The 0, 1, 2, 3, 4, and 5 anomalies that may occur in actual production were tested and combined with machine learning KNN, SVM, and RF algorithms for data modeling and grading. The results show that the lowest accuracy of 0, 1, 2, 3, 4, and 5 possible outliers is 86.67% (KNN), 95.83% (SVM), and 92.5% (RF), respectively. KNN has the lowest classification effect, and SVM has the best. This study overcomes the drawbacks of inefficient destructive detection and unstable manual detection and makes up for the vulnerability of single machine vision to interference from environmental factors. This study can alleviate the challenges caused by fruit wastage and promote the sustainable production and consumption of the fruit industry chain.

Fast detection of moisture content and freshness for loquats using optical fiber spectroscopy

Detection of the moisture content (MC) and freshness for loquats is crucial for achieving optimal taste and economic efficiency. Traditional methods for evaluating the MC and freshness of loquats have disadvantages such as destructive sampling and time-consuming. To investigate the feasibility of rapid and non-destructive detection of the MC and freshness for loquats, optical fiber spectroscopy in the range of 200-1000 nm was used in this study. The full spectra were pre-processed using standard normal variate method, and then, the effective wavelengths were selected using competitive adaptive weighting sampling (CARS) and random frog algorithms. Based on the selected effective wavelengths, prediction models for MC were developed using partial least squares regression (PLSR), multiple linear regression, extreme learning machine, and back-propagation neural network. Furthermore, freshness level discrimination models were established using simplified k nearest neighbor, support vector machine (SVM), and partial least squares discriminant analysis. Regarding the prediction models, the CARS-PLSR model performed relatively better than the other models for predicting the MC, with R 2 P and RPD values of 0.84 and 2.51, respectively. Additionally, the CARS-SVM model obtained superior discrimination performance, with 100% accuracy for both calibration and prediction sets. The results demonstrated that optical fiber spectroscopy technology is an effective tool to fast detect the MC and freshness for loquats.

Rootstock vigor dictates the canopy light environment that regulates metabolite profile and internal fruit quality development in peach

Five rootstock cultivars of differing vigor: vigorous ('Atlas™' and 'Bright's Hybrid® 5'), standard ('Krymsk® 86' and 'Lovell') and dwarfing ('Krymsk® 1') grafted with 'Redhaven' as the scion were studied for their impact on productivity, mid-canopy photosynthetic active radiation transmission (i.e., light availability) and internal fruit quality. Αverage yield (kg per tree) and fruit count increased significantly with increasing vigor (trunk cross sectional area, TCSA). Α detailed peach fruit quality analysis on fruit of equal maturity (based on the index of absorbance difference, IAD) coming from trees with equal crop load (no. of fruit cm-2 of TCSA) characterized the direct impact of rootstock vigor on peach internal quality [dry matter content (DMC) and soluble solids concentration (SSC)]. DMC and SSC increased significantly with decreasing vigor and increasing light availability, potentially due to reduced intra-tree shading and better light distribution within the canopy. Physiologically characterized peach fruit mesocarp was further analyzed by non-targeted metabolite profiling using gas chromatography mass spectrometry (GC-MS). Metabolite distribution was associated with rootstock vigor class, mid-canopy light availability and fruit quality characteristics. Fructose, glucose, sorbose, neochlorogenic and quinic acids, catechin and sorbitol were associated with high light environments and enhanced quality traits, while sucrose, butanoic and malic acids related to low light conditions and inferior fruit quality. These outcomes show that while rootstock genotype and vigor are influencing peach tree productivity and yield, their effect on manipulating the light environment within the canopy also plays a significant role in fruit quality development.

The Authentication of Gayo Arabica Green Coffee Beans with Different Cherry Processing Methods Using Portable LED-Based Fluorescence Spectroscopy and Chemometrics Analysis

Aceh is an important region for the production of high-quality Gayo arabica coffee in Indonesia. In this area, several coffee cherry processing methods are well implemented including the honey process (HP), wine process (WP), and natural process (NP). The most significant difference between the three coffee cherry processing methods is the fermentation process: HP is a process of pulped coffee bean fermentation, WP is coffee cherry fermentation, and NP is no fermentation. It is well known that the WP green coffee beans are better in quality and are sold at higher prices compared with the HP and NP green coffee beans. In this present study, we evaluated the utilization of fluorescence information to discriminate Gayo arabica green coffee beans from different cherry processing methods using portable fluorescence spectroscopy and chemometrics analysis. A total of 300 samples were used (n = 100 for HP, WP, and NP, respectively). Each sample consisted of three selected non-defective green coffee beans. Fluorescence spectral data from 348.5 nm to 866.5 nm were obtained by exciting the intact green coffee beans using a portable spectrometer equipped with four 365 nm LED lamps. The result showed that the fermented green coffee beans (HP and WP) were closely mapped and mostly clustered on the left side of PC1, with negative scores. The non-fermented (NP) green coffee beans were clustered mostly on the right of PC1 with positive scores. The results of the classification using partial least squares-discriminant analysis (PLS-DA), linear discriminant analysis (LDA), and principal component analysis-linear discriminant analysis (PCA-LDA) are acceptable, with an accuracy of more than 80% reported. The highest accuracy of prediction of 96.67% was obtained by using the PCA-LDA model. Our recent results show the potential application of portable fluorescence spectroscopy using LED lamps to classify and authenticate the Gayo arabica green coffee beans according to their different cherry processing methods. This innovative method is more affordable and could be easy to implement (in terms of both affordability and practicability) in the coffee industry in Indonesia.

A high-throughput phenotyping assay for precisely determining stalk crushing strength in large-scale sugarcane germplasm

Sugarcane is a major industrial crop around the world. Lodging due to weak mechanical strength is one of the main problems leading to huge yield losses in sugarcane. However, due to the lack of high efficiency phenotyping methods for stalk mechanical strength characterization, genetic approaches for lodging-resistant improvement are severely restricted. This study attempted to apply near-infrared spectroscopy high-throughput assays for the first time to estimate the crushing strength of sugarcane stalks. A total of 335 sugarcane samples with huge variation in stalk crushing strength were collected for online NIRS modeling. A comprehensive analysis demonstrated that the calibration and validation sets were comparable. By applying a modified partial least squares method, we obtained high-performance equations that had large coefficients of determination (R2 > 0.80) and high ratio performance deviations (RPD > 2.4). Particularly, when the calibration and external validation sets combined for an integrative modeling, we obtained the final equation with a coefficient of determination (R2) and ratio performance deviation (RPD) above 0.9 and 3.0, respectively, demonstrating excellent prediction capacity. Additionally, the obtained model was applied for characterization of stalk crushing strength in large-scale sugarcane germplasm. In a three-year study, the genetic characteristics of stalk crushing strength were found to remain stable, and the optimal sugarcane genotypes were screened out consistently. In conclusion, this study offers a feasible option for a high-throughput analysis of sugarcane mechanical strength, which can be used for the breeding of lodging resistant sugarcane and beyond.

Characterising Colour Feature Descriptors for Ficus carica L. Ripeness Classification Based on Artificial Neural Network (ANN)

Excessive feature dimensions impact the effectiveness of machine learning, computationally expensive and the analysis of feature correlations in the engineering area. This paper uses the colour descriptor to get the most optimal feature to improve time consumption and efficiency. This study investigated Ficus carica L. (figs) with three classification stages. The ripening classification of fig was examined using colour features descriptor with two different colour models, RGB and HSV. In addition, the machine learning classification model based on Artificial Neural Network (ANN) that utilised the Feed-Forward Neural Network (FFNN) model to classify the ripeness of fig is considered in this characterisation. Five different numbers of binning were characterised for RGB and HSV. Both colour feature descriptors were compared in terms of accuracy, sensitivity, precision, and time consumption to identify the dimension of the optimal feature. Based on the result, reducing the size of images will improve the time consumption with comparable accuracy. Moreover, the reduction of features dimension cannot be too small or too big due to inequitable enough to differentiate the ripeness stages and lead to a false error state. The optimal features dimension in binning for RGB was 8 (R/G/B) bins with 96.7% accuracy. Meanwhile, 96.7% accuracy for HSV at 15, 5, and 5 (H, S, V) bins as optimal colour features.

Carbon sufficiency boosts phenylpropanoid biosynthesis early in peach fruit development priming superior fruit quality

Manipulating the crop load in peach trees determines carbon supply and optimum balance between fruit yield and quality potentials. The impact of carbon supply on peach fruit quality was assessed in three development stages (S2, S3, S4) on fruit of equal maturity from trees that were carbon (C) starved (unthinned) and sufficient (thinned). Previous studies determined that primary metabolites of peach fruit mesocarp are mainly linked with developmental processes, thus, the secondary metabolite profile was assessed using non-targeted liquid chromatography mass-spectrometry (LC-MS). Carbon sufficient (C-sufficient) fruit demonstrated superior quality attributes as compared to C-starved fruit. Early metabolic shifts in the secondary metabolome appear to prime quality at harvest. Enhanced C-availability facilitated the increased and consistent synthesis of flavonoids, like catechin, epicatechin and eriodyctiol, via the phenylpropanoid pathway, providing a link between the metabolome and fruit quality, and serving as signatures of C-sufficiency during peach fruit development.

Influence of maturity on bruise detection of peach by structured multispectral imaging

Peaches are easily bruising during all stages of postharvest handling, maturity can affect the characteristics and detection of bruising, which is directly related to the quality and shelf life of peach. The main objective of this research was to investigate the effect of maturity on the early detection of postharvest bruising in peach based on structured multispectral imaging (S-MSI) system. The S-MSI data was measured for bruised peaches, followed by microstructural (CLSM), and biochemical (oxidative browning-related enzyme activities, gene expression, and phenolic compound metabolism) measurements. As the maturity increases, the external impact stress could further induce the accumulation of phenolics through the phenylpropane pathway and pulp oxidative browning, resulting in more pronounced external damage; and the spectral reflectance value of bruised peach was getting smaller, and the spectral waveform gradually flattened out. Three characteristic bands of 781, 824, 867 nm were selected from structured spectra (669-955 nm) related to bruising. The watershed algorithm was adopted for bruise detection, the detection rates for bruised peaches based on three maturity levels (S1-S3) were 91-92%, 90.71-97.43%, and 97.14-99.86%, respectively. This research demonstrated that S-MSI system coupled with watershed algorithm, can enhance our capability of detecting the early bruised peaches of different maturity levels.

Prediction Models for the Content of Calcium, Boron and Potassium in the Fruit of 'Huangguan' Pears Established by Using Near-Infrared Spectroscopy

It has been proved that the imbalance of the proportion of elements of 'Huangguan' pears in the pulp and peel, especially calcium, boron and potassium, may be important factors that can seriously affect the pears' appearance quality and economic benefits. The objective of this study was to predict the content of calcium, boron and potassium in the pulp and peel of 'Huangguan' pears nondestructively and conveniently by using near-infrared spectroscopy (900-1700 nm) technology. Firstly, 12 algorithms were used to preprocess the original spectral data. Then, based on the original and preprocessed spectral data, full-band prediction models were established by using Partial Least Squares Regression and Gradient Boosting Regression Tree. Finally, the characteristic wavelengths were extracted by Genetic Algorithms to establish the characteristic wavelength prediction models. According to the prediction results, the value of the determination coefficient of the prediction sets of the best prediction models for the three elements all reached ideal levels, and the values of their Relative analysis error also showed high levels. Therefore, the micro near-infrared spectrometer based on machine learning can predict the content of calcium, boron and potassium in the pulp and peel of 'Huangguan' pears accurately and quickly. The results also provide an important scientific theoretical basis for further research on the degradation of the quality of 'Huangguan' pears caused by a lack of nutrients.

Detection of Soluble Solids Content in Different Cultivated Fresh Jujubes Based on Variable Optimization and Model Update

To solve the failure problem of the visible/near infrared (VIS/NIR) spectroscopy model, soluble solids content (SSC) detection for fresh jujubes cultivated in different modes was carried out based on the method of variable optimization and model update. Iteratively retained informative variables (IRIV) and successive projections algorithm (SPA) algorithms were used to extract characteristic wavelengths, and least square support vector machine (LS-SVM) was used to establish detection models. Compared with IRIV, IRIV-SPA achieved better performance. Combined with the offset properties of the wavelength, repeated wavelengths were removed, and wavelength recombination was carried out to create a new combination of variables. Using these fused wavelengths, the model was recalibrated based on the Euclidean distance between samples. The LS-SVM detection model of SSC was established using the update method of wavelength fusion-Euclidean distance. Good prediction results were achieved using the proposed model. The determination coefficient (R²), root mean square error (RMSE), and residual predictive deviation (RPD) of the test set on SSC of fresh jujubes cultivated in the open field were 0.82, 1.49%, and 2.18, respectively. The R², RMSE, and RPD of the test set on SSC of fresh jujubes cultivated in the rain shelter were 0.81, 1.44%, and 2.17, respectively. This study realized the SSC detection of fresh jujubes with different cultivation and provided a method for the establishment of a robust VIS/NIR detection model for fruit quality, effectively addressing the industry need for identifying jujubes grown in the open field.

Effects of Orientations and Regions on Performance of Online Soluble Solids Content Prediction Models Based on Near-Infrared Spectroscopy for Peaches

Predicting the soluble solid content (SSC) of peaches based on visible/near infrared spectroscopy has attracted widespread attention. Due to the anisotropic structure of peach fruit, spectra collected from different orientations and regions of peach fruit will bring variations in the performance of SSC prediction models. In this study, the effects of spectra collection orientations and regions on online SSC prediction models for peaches were investigated. Full transmittance spectra were collected in two orientations: stem-calyx axis vertical (Orientation1) and stem-calyx axis horizontal (Orientation2). A partial least squares (PLS) method was used to evaluate the spectra collected in the two orientations. Then, each peach fruit was divided into three parts. PLS was used to evaluate the corresponding spectra of combinations of these three parts. Finally, effective wavelengths were selected using the successive projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS). Both orientations were ideal for spectra acquisition. Regions without peach pit were ideal for modeling, and the effective wavelengths selected by the SPA led to better performance. The correlation coefficient and root mean square error of validation of the optimal models were 0.90 and 0.65%, respectively, indicating that the optimal model has potential for online prediction of peach SSC.

Spectral and image analysis of hyperspectral data for internal and external quality assessment of peach fruit

Hyperspectral imaging was attempted to evaluate the internal and external quality of 'Feicheng' peach by providing the spectral and spatial data simultaneously. Mask-image was created from hyperspectral image at 810 nm and used to segment the fruit region where the average spectrum, after area normalization, was obtained for soluble solids content (SSC) and firmness evaluation. Pixel size and area were used for diameter and weight estimation. Then effective wavelengths were selected by competitive adaptive reweighted sampling (CARS) and random frog (RF), and employed to develop multiple linear regression (MLR) models. The more effective prediction performances emerged from CARS-MLR model withR_V² = 0.841, RMSEV = 0.546, RPD = 2.51 for SSC andR_V² = 0.826, RMSEV = 1.008, RPD = 2.401 for firmness, followed by creating pixel-wise and object-wise visualization maps for quantifying SSC and firmness. Furthermore, peach diameter was estimated by calculating the minimum bounding rectangle with an average percentage error of 1.01 %, and the MLR model forweightpredictionachieveda good performance ofR_V² = 0.957, RMSEV = 9.203, and RPD = 4.819. The overall results showed that hyperspectral imaging could be used as an effective and non-destructive tool for evaluating the internal and external quality attributes of 'Feicheng' peach, and provided a holistic approach to develop online grading systems for quality tiers identification.

Prediction of the Maturity of Greenhouse Grapes Based on Imaging Technology

To predict grape maturity in solar greenhouses, a plant phenotype-monitoring platform (Phenofix, France) was used to obtain RGB images of grapes from expansion to maturity. Horizontal and longitudinal diameters, compactness, soluble solid content (SSC), titratable acid content, and the SSC/acid of grapes were measured and evaluated. The color values (R, G, B, H, S, and I) of the grape skin were determined and subjected to a back-propagation neural network algorithm (BPNN) to predict grape maturity. The results showed that the physical and chemical properties (PCP) of the three varieties of grapes changed significantly during the berry expansion stage and the color-changing maturity stage. According to the normalized rate of change of the PCP indicators, the ripening process of the three varieties of grapes could be divided into two stages: an immature stage (maturity coefficient Mc < 0.7) and a mature stage (after which color changes occurred) (0.7 ≤ Mc < 1). When predicting grape maturity based on the R, G, B, H, I, and S color values, the R, G, and I as well as G, H, and I performed well for Drunk Incense, Muscat Hamburg, and Xiang Yue grape maturity prediction. The GPI ranked in the top three (up to 0.87) when the above indicators were used in combination with BPNN to predict the grape Mc by single-factor and combined-factor analysis. The results showed that the prediction accuracy (RG and HI) of the two-factor combination was better for Drunk Incense, Muscat Hamburg, and Xiang Yue grapes (with recognition accuracies of 79.3%, 78.2%, and 79.4%, respectively), and all of the predictive values were higher than those of the single-factor predictions. Using a confusion matrix to compare the accuracy of the Mc's predictive ability under the two-factor combination method, the prediction accuracies were in the following order: Xiang Yue (88%) > Muscat Hamburg (81.3%) > Drunk Incense (76%). The results of this study provide an effective way to predict the ripeness of grapes in the greenhouse.

Detection of heavy metal lead in lettuce leaves based on fluorescence hyperspectral technology combined with deep learning algorithm

The feasibility analysis of fluorescence hyperspectral imaging technology was studied for the detection of lead content in lettuce leaves. Further, Monte Carlo optimized wavelet transform stacked auto-encoders (WT-MC-SAE) was proposed for dimensionality reduction and depth feature extraction of fluorescence spectral data. The fluorescence hyperspectral images of 2800 lettuce leaf samples were selected and the whole lettuce leaf was used as the region of interest (ROI) to extract the fluorescence spectrum. Five different pre-processing algorithms were used to pre-process the original ROI spectral data including standard normalized variable (SNV), first derivative (1st Der), second derivative (2ndDer), third derivative (3rd Der) and fourth derivative (4th Der). Moreover, wavelet transform stacked auto-encoders (WT-SAE) and WT-MC-SAE were used for data dimensionality reduction, and support vector machine regression (SVR) was used for modeling analysis. Among them, 4th Der tends to be the most useful fluorescence spectral data for Pb content detection at 0.067 ∼ 1.400 mg/kg in lettuce leaves, with Rc2 of 0.9802, RMSEC of 0.02321 mg/kg, Rp2 of 0.9467, RMSEP of 0.04017 mg/kg and RPD of 3.273, and model scale (the number of nodes in the input layer, hidden layer and output layer) was 407-314-286-121-76 under the fifth level of wavelet decomposition. Further studies showed that WT-MC-SAE realizes the depth feature extraction of the fluorescence spectrum, and it is of great significance to use fluorescence hyperspectral imaging to realize the quantitative detection of lead in lettuce leaves.

Branch Numbers and Crop Load Combination Effects on Production and Fruit Quality of Flat Peach Cultivars (Prunus persica (L.) Batsch) Trained as Catalonian Vase

Thinning and pruning are expensive cultural practices in peach cultivation, but essential to obtain adequate production. This study evaluated the effects of combining two pruning (four and six scaffold branches) and three thinning (low, medium, and high crop load) levels on yield and fruit quality of four different flat peach cultivars, trained as Catalonian vase in 2017-2018 in Italy. Productive (average fruit weight, plant total production, and fruit circumference), qualitative (fruit firmness and overcolor, Soluble Solids Content, and Titratable Acidity), and nutritional (Total Antioxidant Capacity, and Total Phenol Content) parameters were evaluated. For productive parameters, a high crop load level led to a decrease in fruit weight and circumference, while a high crop load resulted in higher plant yield. Regarding the qualitative parameters, fruit SSC significantly increased with the diminution of the crop load level in both years of study, while TA was not influenced by crop load and number of branches. Both the total antioxidant capacity and the polyphenol content decreased with an increase in branches number. The findings derived from this study will help growers to select the most suitable combination among genotypes and plant management, to obtain the desired productive or qualitative goals.

Nutritional composition changes in bell pepper as affected by the ripening stage of fruits at harvest or postharvest storage and assessed non-destructively

BACKGROUND

Nutritional quality in bell pepper is related to the ripening stage of the fruit at harvest and postharvest storage. Its determination requires time-consuming, tissue-destructive, analytical laboratory techniques. The objective of this study was to investigate the effect of ripening stage and of postharvest storage period on fruit nutritional quality, and whether it is feasible to develop reliable models for assessing the nutritional components in peppers using non-destructive methods. The dry matter, soluble solids, ascorbic acid, phenolics, chlorophylls, carotenoids and the total antioxidant capacity were determined in bell pepper fruits at six ripening stages, from green to full red, during storage at 10 °C for 8 days. Color, chlorophyll fluorescence, visible/near infrared (Vis/NIR) spectroscopy, red-green-blue (R-G-B) and red-green-near infrared (R-G-NIR) digital imaging were tested for assessing the nutritional quality of peppers.

RESULTS

The nutritional composition was mainly affected by the ripening stage of bell pepper fruits at harvest and only to a small degree by the storage period. Indeed, the more advanced ripening stage of fruit at harvest resulted in superior nutritional quality. Most of the non-destructive techniques reliably predicted the internal quality of the fruit. The genetic algorithm (GA), the variable importance in projection (VIP) scores, and the variable inflation factor (VIF) tests identified nine distinct regions and four specific wavelengths on the whole visible/NIR electromagnetic spectrum that exhibited the most significant effect in the assessment of the nutritional components.

CONCLUSION

It is possible to predict individual nutritional components in bell pepper fruit reliably and non-destructively, and irrespective of the ripening stage of fruits at harvest. © 2021 Society of Chemical Industry.

Non-destructive prediction of the hotness of fresh pepper with a single scan using portable near infrared spectroscopy and a variable selection strategy

There has been no study on using near-infrared spectroscopy (NIRS) to predict the hotness of fresh pepper. This study is aimed at developing a non-destructive and accurate method for determining the hotness of fresh peppers using portable NIRS and the variable selection strategy. Spectra from different locations on samples were obtained non-destructively with a single scan. Quantitative models were established using partial least squares (PLS) with a variable selection method or fusion method. The results showed that near-stalk was the best spectral acquisition location for quantitative analysis. The variable selection strategy allows the selection of targeted characteristic variables and improves the results. A fusion method, namely variable adaptive boosting partial least squares (VABPLS), was selected for optimal prediction of the performance. In the optimized model, the root mean square errors of prediction for the validation set (RMSEPvs) of capsaicin, dihydrocapsaicin and pungency degree were 0.295, 0.143 and 47.770, respectively, while the root mean square errors of prediction for the prediction set (RMSEPps) collected one month later were 0.273, 0.346 and 75.524, respectively.

Application of near-infrared spectroscopy for the nondestructive analysis of wheat flour: A review

The quality and safety of wheat flour are of public concern since they are related to the quality of flour products and human health. Therefore, efficient and convenient analytical techniques are needed for the quality and safety controls of wheat flour. Near-infrared (NIR) spectroscopy has become an ideal technique for assessing the quality and safety of wheat flour, as it is a rapid, efficient and nondestructive method. The application of NIR spectroscopy in the quality and safety analysis of wheat flour is addressed in this review. First, we briefly summarize the basic knowledge of NIR spectroscopy and chemometrics. Then, recent advances in the application of NIR spectroscopy for chemical composition, technological parameters, and safety analysis are presented. Finally, the potential of NIR spectroscopy is discussed. Combined with chemometric methods, NIR spectroscopy has been used to detect chemical composition, technological parameters, deoxynivalenol, adulterants and additives of wheat flour. Furthermore, NIR spectroscopy has shown great potential for the rapid and online analysis of the quality and safety of wheat flour. It is anticipated that the current review will serve as a reference for the future analysis of wheat flour by NIR spectroscopy to ensure the quality and safety of flour products.

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NIR spectroscopy is an ideal technique for analysis of wheat flour due to its rapid and nondestructive nature.

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Use of NIR spectroscopy for chemical composition, technological parameters, and safety analysis.

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Online and handheld NIR spectrometers for wheat flour detection are the future trends.

Soft Mango Firmness Assessment Based on Rayleigh Waves Generated by a Laser-Induced Plasma Shock Wave Technique

Many methods based on acoustic vibration characteristics have been studied to indirectly assess fruit ripeness via fruit firmness. Among these, the frequency of the ₀S₂ vibration mode measured on the equator has been examined, but soft-flesh fruit do not show the ₀S₂ vibration mode. In this study, a Rayleigh wave is generated on a soft mango fruit using the impulse excitation force generated by a laser-induced plasma shock wave technique. Then, the flesh firmness of mangoes is assessed in a non-contact and non-destructive manner by observing the Rayleigh wave propagation velocity because it is correlated with the firmness (shear elasticity), density, and Poisson's ratio of an object. If the changes in the density and Poisson's ratio are small enough to be ignored during storage, then the Rayleigh wave propagation velocity is strongly correlated to fruit firmness. Here, we measure the Rayleigh wave propagation velocity and investigate the effect of storage time. Specifically, we investigate the changes in firmness caused by ripening. The Rayleigh wave propagation velocity on the equator of Kent mangoes tended to decrease by over 4% in 96 h. The Rayleigh wave measured on two different lines propagated independent distance and showed a different change rate of propagation velocity during 96-h storage. Furthermore, we consider the reliability of our method by investigating the interaction of a mango seed on the Rayleigh wave propagation velocity.

Shotgun proteomics of peach fruit reveals major metabolic pathways associated to ripening

BACKGROUND

Fruit ripening in Prunus persica melting varieties involves several physiological changes that have a direct impact on the fruit organoleptic quality and storage potential. By studying the proteomic differences between the mesocarp of mature and ripe fruit, it would be possible to highlight critical molecular processes involved in the fruit ripening.

RESULTS

To accomplish this goal, the proteome from mature and ripe fruit was assessed from the variety O'Henry through shotgun proteomics using 1D-gel (PAGE-SDS) as fractionation method followed by LC/MS-MS analysis. Data from the 131,435 spectra could be matched to 2740 proteins, using the peach genome reference v1. After data pre-treatment, 1663 proteins could be used for comparison with datasets assessed using transcriptomic approaches and for quantitative protein accumulation analysis. Close to 26% of the genes that code for the proteins assessed displayed higher expression at ripe fruit compared to other fruit developmental stages, based on published transcriptomic data. Differential accumulation analysis between mature and ripe fruit revealed that 15% of the proteins identified were modulated by the ripening process, with glycogen and isocitrate metabolism, and protein localization overrepresented in mature fruit, as well as cell wall modification in ripe fruit. Potential biomarkers for the ripening process, due to their differential accumulation and gene expression pattern, included a pectin methylesterase inhibitor, a gibbellerin 2-beta-dioxygenase, an omega-6 fatty acid desaturase, a homeobox-leucine zipper protein and an ACC oxidase. Transcription factors enriched in NAC and Myb protein domains would target preferentially the genes encoding proteins more abundant in mature and ripe fruit, respectively.

CONCLUSIONS

Shotgun proteomics is an unbiased approach to get deeper into the proteome allowing to detect differences in protein abundance between samples. This technique provided a resolution so that individual gene products could be identified. Many proteins likely involved in cell wall and sugar metabolism, aroma and color, change their abundance during the transition from mature to ripe fruit.

Discrimination of Fresh Tobacco Leaves with Different Maturity Levels by Near-Infrared (NIR) Spectroscopy and Deep Learning

The maturity affects the yield, quality, and economic value of tobacco leaves. Leaf maturity level discrimination is an important step in manual harvesting. However, the maturity judgment of fresh tobacco leaves by grower visual evaluation is subjective, which may lead to quality loss and low prices. Therefore, an objective and reliable discriminant technique for tobacco leaf maturity level based on near-infrared (NIR) spectroscopy combined with a deep learning approach of convolutional neural networks (CNNs) is proposed in this study. To assess the performance of the proposed maturity discriminant model, four conventional multiclass classification approaches-K-nearest neighbor (KNN), backpropagation neural network (BPNN), support vector machine (SVM), and extreme learning machine (ELM)-were employed for a comparative analysis of three categories (upper, middle, and lower position) of tobacco leaves. Experimental results showed that the CNN discriminant models were able to precisely classify the maturity level of tobacco leaves for the above three data sets with accuracies of 96.18%, 95.2%, and 97.31%, respectively. Moreover, the CNN models with strong feature extraction and learning ability were superior to the KNN, BPNN, SVM, and ELM models. Thus, NIR spectroscopy combined with CNN is a promising alternative to overcome the limitations of sensory assessment for tobacco leaf maturity level recognition. The development of a maturity-distinguishing model can provide an accurate, reliable, and scientific auxiliary means for tobacco leaf harvesting.

Elaboration of novel and comprehensive protocols toward determination of textural properties and other sensorial attributes of canning peach fruit

Peach (Prunus persica) products are destined for fresh consumption or are being consumed after processing in various forms. Despite its huge economic importance, no standardized protocols to define sensorial attributes and mechanical properties of canned peaches exist. Thus, the aim of the current study was dual and included the setting up of a list of sensorial descriptors and the elaboration of a toolkit to evaluate the textural properties of canned peaches using large deformation mechanical testing. A standardized vocabulary ("consensus language") was initially developed toward the determination and quantification of 15 sensorial attributes through a descriptive quantitative analysis (QDA) approach. Textural properties were additionally evaluated with a TA-XT Plus texture analyzer by applying three discrete large deformation tests [(a) puncture test with a flat cylindrical probe; (b) texture profile analysis (TPA) with a flat compression plunger; and (c) Kramer shear test (KST) cell with a bladed fixture]; that is, a total of nine textural properties, namely, "puncture firmness" (individual halves), "Kramer" hardness (applied in a complex mixture of peach slices), "TPA" hardness (central section of halves), fracturability, consistency, cohesiveness, springiness, chewiness, and total hardness were assessed. We hereby present novel protocols that encompass the comprehensive determination of sensorial and textural properties. The established protocols, providing complementary information, are readily applicable to the canning industry in setting up qualitative tests to determine product shelf life as well as to assist on going breeding programs for the evaluation of new candidate clingstone cultivars destined for canning purposes.

Early metabolic priming under differing carbon sufficiency conditions influences peach fruit quality development

Crop load management is an important preharvest factor to balance yield, quality, and maturation in peach. However, few studies have addressed how preharvest factors impact metabolism on fruit of equal maturity. An experiment was conducted to understand how carbon competition impacts fruit internal quality and metabolism in 'Cresthaven' peach trees by imposing distinct thinning severities. Fruit quality was evaluated at three developmental stages (S2, S3, S4), while controlling for equal maturity using non-destructive visual to near-infrared spectroscopy. Non-targeted metabolite profiling was used to characterize fruit at each developmental stage from trees that were unthinned (carbon starvation) or thinned (carbon sufficiency). Carbon sufficiency resulted in significantly higher fruit dry matter content and soluble solids concentration at harvest when compared to the carbon starved, underscoring the true impact of carbon manipulation on fruit quality. Significant differences in the fruit metabolome between treatments were observed at S2 when phenotypes were similar, while less differences were observed at S4 when the carbon sufficient fruit exhibited a superior phenotype. This suggests a potential metabolic priming effect on fruit quality when carbon is sufficiently supplied during early fruit growth and development. In particular, elevated levels of catechin may suggest a link between secondary/primary metabolism and fruit quality development.

Maturity Prediction in Yellow Peach (Prunus persica L.) Cultivars Using a Fluorescence Spectrometer

Technology for rapid, non-invasive and accurate determination of fruit maturity is increasingly sought after in horticultural industries. This study investigated the ability to predict fruit maturity of yellow peach cultivars using a prototype non-destructive fluorescence spectrometer. Collected spectra were analysed to predict flesh firmness (FF), soluble solids concentration (SSC), index of absorbance difference (I_AD), skin and flesh colour attributes (i.e., a* and H°) and maturity classes (immature, harvest-ready and mature) in four yellow peach cultivars—‘August Flame’, ‘O’Henry’, ‘Redhaven’ and ‘September Sun’. The cultivars provided a diverse range of maturity indices. The fluorescence spectrometer consistently predicted I_AD and skin colour in all the cultivars under study with high accuracy (Lin’s concordance correlation coefficient > 0.85), whereas flesh colour’s estimation was always accurate apart from ‘Redhaven’. Except for ‘September Sun’, good prediction of FF and SSC was observed. Fruit maturity classes were reliably predicted with a high likelihood (F1-score = 0.85) when samples from the four cultivars were pooled together. Further studies are needed to assess the performance of the fluorescence spectrometer on other fruit crops. Work is underway to develop a handheld version of the fluorescence spectrometer to improve the utility and adoption by fruit growers, packhouses and supply chain managers.