Nondestructive measurement of fruit and vegetable quality

Application of Near Infrared Spectroscopy for the Rapid Assessment of Nutritional Quality of Different Strawberry Cultivars

Strawberry is the most cultivated berry fruit globally and it is really appreciated by consumers because of its characteristics, mainly bioactive compounds with antioxidant properties. During the breeding process, it is important to assess the quality characteristics of the fruits for a better selection of the material, but the conventional approaches involve long and destructive lab techniques. Near infrared spectroscopy (NIR) could be considered a valid alternative for speeding up the breeding process and is not destructive. In this study, a total of 216 strawberry fruits belonging to four different cultivars have been collected and analyzed with conventional lab analysis and NIR spectroscopy. In detail, soluble solid content, acidity, vitamin C, anthocyanin, and phenolic acid have been determined. Partial least squares discriminant analysis (PLS-DA) models have been developed to classify strawberry fruits belonging to the four genotypes according to their quality and nutritional properties. NIR spectroscopy could be considered a valid non-destructive phenotyping method for monitoring the nutritional parameters of the fruit and ensuring the fruit quality, speeding up the breeding program.

Fabrication and characterization of porous tissue-mimicking optical phantoms as a tool for optical sensor validation

This paper presents porous polydimethylsiloxane (PDMS) optical phantoms with tunable microstructural and optical properties to mimic porous biological tissues (e.g., fruit) during the design and optimization of novel optical setups. A well connected salt network formed using salt particles of various size distributions was used to obtain porous PDMS phantoms of different porous features including porosity, pore size distribution, pore number density and pore connectivity. These microstructural features are strongly related to the light scattering from the phantom where a higher reduced scattering coefficient ( μ s ' ) was observed from the porous PDMS phantom with a higher number of small pores compared to the optical phantom with a lower number of larger pores. The prepared phantoms were used to validate GASMAS (gas in scattering media absorption spectroscopy) H₂ O and O₂ sensors by quantifying the optical path length through the pores and the O₂ concentration inside the pores.

Feasibility Study of Combining Hyperspectral Imaging with Deep Learning for Chestnut-Quality Detection

The rapid detection of chestnut quality is a critical aspect of chestnut processing. However, traditional imaging methods pose a challenge for chestnut-quality detection due to the absence of visible epidermis symptoms. This study aims to develop a quick and efficient detection method using hyperspectral imaging (HSI, 935-1720 nm) and deep learning modeling for qualitative and quantitative identification of chestnut quality. Firstly, we used principal component analysis (PCA) to visualize the qualitative analysis of chestnut quality, followed by the application of three pre-processing methods to the spectra. To compare the accuracy of different models for chestnut-quality detection, traditional machine learning models and deep learning models were constructed. Results showed that deep learning models were more accurate, with FD-LSTM achieving the highest accuracy of 99.72%. Moreover, the study identified important wavelengths for chestnut-quality detection at around 1000, 1400 and 1600 nm, to improve the efficiency of the model. The FD-UVE-CNN model achieved the highest accuracy of 97.33% after incorporating the important wavelength identification process. By using the important wavelengths as input for the deep learning network model, recognition time decreased on average by 39 s. After a comprehensive analysis, FD-UVE-CNN was deter-mined to be the most effective model for chestnut-quality detection. This study suggests that deep learning combined with HSI has potential for chestnut-quality detection, and the results are encouraging.

The Application of Hyperspectral Imaging Technologies for the Prediction and Measurement of the Moisture Content of Various Agricultural Crops during the Drying Process

Drying is one of the common procedures in the food processing steps. The moisture content (MC) is also of crucial significance in the evaluation of the drying technique and quality of the final product. However, conventional MC evaluation methods suffer from several drawbacks, such as long processing time, destruction of the sample and the inability to determine the moisture of single grain samples. In this regard, the technology and knowledge of hyperspectral imaging (HSI) were addressed first. Then, the reports on the use of this technology as a rapid, non-destructive, and precise method were explored for the prediction and detection of the MC of crops during their drying process. After spectrometry, researchers have employed various pre-processing and merging data techniques to decrease and eliminate spectral noise. Then, diverse methods such as linear and multiple regressions and machine learning were used to model and predict the MC. Finally, the best wavelength capable of precise estimation of the MC was reported. Investigation of the previous studies revealed that HSI technology could be employed as a valuable technique to precisely control the drying process. Smart dryers are expected to be commercialised and industrialised soon by the development of portable systems capable of an online MC measurement.

Developing non-invasive 3D quantificational imaging for intelligent coconut analysis system with X-ray

BACKGROUND

As one of the largest drupes in the world, the coconut has a special multilayered structure and a seed development process that is not yet fully understood. On the one hand, the special structure of the coconut pericarp prevents the development of external damage to the coconut fruit, and on the other hand, the thickness of the coconut shell makes it difficult to observe the development of bacteria inside it. In addition, coconut takes about 1 year to progress from pollination to maturity. During the long development process, coconut development is vulnerable to natural disasters, cold waves, typhoons, etc. Therefore, nondestructive observation of the internal development process remains a highly important and challenging task. In this study, We proposed an intelligent system for building a three-dimensional (3D) quantitative imaging model of coconut fruit using Computed Tomography (CT) images. Cross-sectional images of coconut fruit were obtained by spiral CT scanning. Then a point cloud model was built by extracting 3D coordinate data and RGB values. The point cloud model was denoised using the cluster denoising method. Finally, a 3D quantitative model of a coconut fruit was established.

RESULTS

The innovations of this work are as follows. 1) Using CT scans, we obtained a total of 37,950 non-destructive internal growth change maps of various types of coconuts to establish a coconut data set called "CCID", which provides powerful graphical data support for coconut research. 2) Based on this data set, we built a coconut intelligence system. By inputting a batch of coconut images into a 3D point cloud map, the internal structure information can be ascertained, the entire contour can be drawn and rendered according to need, and the long diameter, short diameter and volume of the required structure can be obtained. We maintained quantitative observation on a batch of local Hainan coconuts for more than 3 months. With 40 coconuts as test cases, the high accuracy of the model generated by the system is proven. The system has a good application value and broad popularization prospects in the cultivation and optimization of coconut fruit.

CONCLUSION

The evaluation results show that the 3D quantitative imaging model has high accuracy in capturing the internal development process of coconut fruits. The system can effectively assist growers in internal developmental observations and in structural data acquisition from coconut, thus providing decision-making support for improving the cultivation conditions of coconuts.

Application of a Cost-Effective Visible/Near Infrared Optical Prototype for the Measurement of Qualitative Parameters of Chardonnay Grapes

In this paper, a cost-effective visible/near infrared optical prototype was tested for grape maturity monitoring. The device was used to quantify the qualitative parameters of Chardonnay grapes, based on the combination of spectroscopic data and the creation of predictive models. The optical acquisitions were performed directly in the field through the use of 12 wavelengths in the vis/NIR range, i.e., 450, 500, 550, 570, 600, 610, 650, 680, 730, 760, 810 and 860 nanometers. The prediction of the qualitative parameters was carried out through a multivariate model, partial least square (PLS) regression technique and built knowing the real values of the parameters, i.e., total soluble solids (TSS), titratable acidity (TA) and pH measured through the reference laboratory analyses. Sampling included two harvest years. The most efficient model was the one for TSS evaluation that gave a R2 = 0.87 (independent test set validation). The results demonstrated that the optical device is able to provide useful information about the ripening parameters of Chardonnay grapes directly in the field in order to predict its correct maturation stage and, therefore, support operators in rapid and objective decision making. Overall, the use of the prototype promotes a sustainable approach and viticulture 4.0.

Optical characterization of the liver tissue affected by fibrolamellar hepatocellular carcinoma based on internal filters of laser-induced fluorescence

Laser-induced fluorescence (LIF) spectroscopy has recently gained regards for diagnosis of the cancer in various tissues of the human body. This method in its conventional form, when used for assay of highly scattering media, encounters a lot of noise due to multiple scattering and inner filter effects which overshadows the sensitivity and specificity of the method. Here, angular dependence of the LIF spectral shift due to the reabsorption events have been investigated for characterization of the bio-tissues. The aim was to determine the tissue morphological changeovers due to the cancer progression. The assessment of a rare type of the liver cancer i. e. fibrolamellar hepatocellular carcinoma revealed the significant difference in optical anisotropy of the parenchyma and liver tumor. As a result, utilizing LIF spectroscopy as a fast, highly sensitive and easy-to-use method one can evaluate the optical anisotropy for diagnosing tissues during the cancer progression.

A review on colorimetric indicators for monitoring product freshness in intelligent food packaging: Indicator dyes, preparation methods, and applications

Intelligent food packaging system exhibits enhanced communication function by providing dynamic product information to various stakeholders (e.g., consumers, retailers, distributors) in the supply chain. One example of intelligent packaging involves the use of colorimetric indicators, which when subjected to external stimuli (e.g., moisture, gas/vapor, electromagnetic radiation, temperature), display discernable color changes that can be correlated with real-time changes in product quality. This type of interactive packaging system allows continuous monitoring of product freshness during transportation, distribution, storage, and marketing phases. This review summarizes the colorimetric indicator technologies for intelligent packaging systems, emphasizing on the types of indicator dyes, preparation methods, applications in different food products, and future considerations. Both food and nonfood indicator materials integrated into various carriers (e.g., paper-based substrates, polymer films, electrospun fibers, and nanoparticles) with material properties optimized for specific applications are discussed, targeting perishable products, such as fresh meat and fishery products. Colorimetric indicators can supplement the traditional "Best Before" date label by providing real-time product quality information to the consumers and retailers, thereby not only ensuring product safety, but also promising in reducing food waste. Successful scale-up of these intelligent packaging technologies to the industrial level must consider issues related to regulatory approval, consumer acceptance, cost-effectiveness, and product compatibility.

Making Sense of Light: The Use of Optical Spectroscopy Techniques in Plant Sciences and Agriculture

As a result of the development of non-invasive optical spectroscopy, the number of prospective technologies of plant monitoring is growing. Being implemented in devices with different functions and hardware, these technologies are increasingly using the most advanced data processing algorithms, including machine learning and more available computing power each time. Optical spectroscopy is widely used to evaluate plant tissues, diagnose crops, and study the response of plants to biotic and abiotic stress. Spectral methods can also assist in remote and non-invasive assessment of the physiology of photosynthetic biofilms and the impact of plant species on biodiversity and ecosystem stability. The emergence of high-throughput technologies for plant phenotyping and the accompanying need for methods for rapid and non-contact assessment of plant productivity has generated renewed interest in the application of optical spectroscopy in fundamental plant sciences and agriculture. In this perspective paper, starting with a brief overview of the scientific and technological backgrounds of optical spectroscopy and current mainstream techniques and applications, we foresee the future development of this family of optical spectroscopic methodologies.

Predicting sensitivity of recently harvested tomatoes and tomato sepals to future fungal infections

Tomato is an important commercial product which is perishable by nature and highly susceptible to fungal incidence once it is harvested. Not all tomatoes are equally vulnerable to pathogenic fungi, and an early detection of the vulnerable ones can help in taking timely preventive actions, ranging from isolating tomato batches to adjusting storage conditions, but also in making right business decisions like dynamic pricing based on quality or better shelf life estimate. More importantly, early detection of vulnerable produce can help in taking timely actions to minimize potential post-harvest losses. This paper investigates Near-infrared (NIR) hyperspectral imaging (1000-1700 nm) and machine learning to build models to automatically predict the susceptibility of sepals of recently harvested tomatoes to future fungal infections. Hyperspectral images of newly harvested tomatoes (cultivar Brioso) from 5 different growers were acquired before the onset of any visible fungal infection. After imaging, the tomatoes were placed under controlled conditions suited for fungal germination and growth for a 4-day period, and then imaged using normal color cameras. All sepals in the color images were ranked for fungal severity using crowdsourcing, and the final severity of each sepal was fused using principal component analysis. A novel hyperspectral data processing pipeline is presented which was used to automatically segment the tomato sepals from spectral images with multiple tomatoes connected via a truss. The key modelling question addressed in this research is whether there is a correlation between the hyperspectral data captured at harvest and the fungal infection observed 4 days later. Using 10-fold and group k-fold cross-validation, XG-Boost and Random Forest based regression models were trained on the features derived from the hyperspectral data corresponding to each sepal in the training set and tested on hold out test set. The best model found a Pearson correlation of 0.837, showing that there is strong linear correlation between the NIR spectra and the future fungal severity of the sepal. The sepal specific predictions were aggregated to predict the susceptibility of individual tomatoes, and a correlation of 0.92 was found. Besides modelling, focus is also on model interpretation, particularly to understand which spectral features are most relevant to model prediction. Two approaches to model interpretation were explored, feature importance and SHAP (SHapley Additive exPlanations), resulting in similar conclusions that the NIR range between 1390-1420 nm contributes most to the model's final decision.

Enhanced visible/near-infrared spectroscopic data for prediction of quality attributes in Cucurbitaceae commodities

Spectra data of 300 samples from 6 Cucurbitaceae commodities, including zucchini, bitter gourd, ridge gourd, melon, chayote, and cucumber, were recorded using a handheld visible/near-infrared (Vis/NIR) instrument. Vis/NIR data were obtained in the form of absorbance spectra data at a wavelength of 381–1065 nm. The spectral data has the potential to be reused to predict quality attributes in the form of soluble solids and water content on several Cucurbitaceae commodities. The accuracy of the Vis/NIR calibration model can be increased by applying spectra preprocessing, for example, second derivative savitzky-golay (dg2). The calibration model was developed using the principal component regression (PCR) method on RAW and dg2 spectra. The enhanced Vis/NIR dataset can be used to evaluate the inner quality attributes of intact fruits in a rapid, non-destructive manner.

Non-Destructive Quality Measurement for Three Varieties of Tomato Using VIS/NIR Spectroscopy

The non-destructive visible/near-infrared (VIS/NIR) spectroscopy is a promising technique in determining the quality of agricultural commodities. Therefore, this study aimed to examine the ability of VIS/NIR spectroscopy (550–1100 nm) to distinguish between three different varieties of tomato (i.e., Ekram, Harver and Izmer), as well as to predict the quality parameters of tomato, such as soluble solids content (SSC), titratable acidity (TA), taste (SSC/TA) and firmness. Ninety intact samples from three tomato varieties were used. These samples were examined using VIS/NIR spectroscopy and quality parameters were also measured using traditional methods. Principal component analysis (PCA) and partial least square (PLS) were carried out. The results of PCA showed the ability of VIS/NIR spectroscopy to distinguish between the three varieties, where two PCs explained about 99% of the total variance in both calibration and validation sets. Moreover, PLS showed the possibility of modelling quality parameters. The correlation coefficient (R2) and the ratio of performance deviation (RPD) for all quality parameters (except for firmness) were found to be higher than 0.85 and 2.5, respectively. Thus, these results indicate that the VIS/NIR spectroscopy can be used to discriminate between different varieties of tomato and predict their quality parameters.

Nutritive quality prediction of peaches during storage

Peaches (Prunus persica L. Batsch) are commonly consumed fruits with high nutritional value. We evaluated the nutritive qualities of peach fruit during storage. Heatmap analysis showed that protein, ash, and crude fiber contents clustered together, whereas fat and reducing sugars clustered separately. We then classified the nutrients into two clusters; cluster 1 showed low fat and reducing sugar levels and high protein, crude fiber, and ash levels, whereas cluster 2 showed high fat and reducing sugar levels and low protein, cruder fiber, and ash levels. Partial least squares regression and random forest analyses showed accuracies of 67% and 61%, respectively. Spectra at 1,439 and 1,440 nm indicated reducing sugars, and the spectrum at 2,172 nm indicated protein. Thus, Fourier transform-near infrared spectroscopy could predict the two clusters based on five nutritive qualities. Our findings may help to establish guidelines for promoting the acceptability of peach fruits among consumers.

Investigating the Effect of Different Drying Strategies on the Quality Parameters of Daucus carota L. Using Dynamic Process Control and Measurement Techniques

The present work aims to improve the understanding of the effect of different drying strategies at varying temperatures on the dynamic drying behaviour and quality of organic products such as carrots using non-invasive measurement techniques. For this purpose, carrot slices of 3 mm thickness were dried under three different strategies namely air temperature controlled (A), product temperature controlled (P) and stepwise temperature controlled (S) at different temperatures (50 °C, 60 °C and 70 °C) to measure and analyse the changes in moisture content, colour, total carotenoid retention, water activity, rehydration ratio and specific energy consumption. From the investigation performed, it was incurred that the application of different drying strategies influences rather significantly both the product quality as well as the overall process efficiency. Modelling the drying curves deemed Page model to be a good fit for all the strategies with R2adj = 0.99 and RMSE = 0.01. The results also show that implementing strategy P not only led to shorter drying times but also led to higher total carotenoid retention within the samples (TCR = 0.59–0.73). Colour changes, however, were observed to be maximum with strategy P as compared to strategy A and strategy S. Furthermore, the use of a non-invasive measurement technique such as infrared camera proved to be reliable in order to detect the phase transition of the product during the drying process.

Recent progress of nondestructive techniques for fruits damage inspection: a review

In the process of growing, harvesting, and storage, fruits are vulnerable to mechanical damage, microbial infections, and other types of damage, which not only reduce the quality of fruits, increase the risk of fungal infections, in turn greatly affect food safety, but also sharply reduce economic benefits. Hence, it is essential to identify damaged fruits in time. Rapid and nondestructive detection of fruits damage is in great demand. In this paper, the latest research progresses on the detection of fruits damage by nondestructive techniques, including visible/near-infrared spectroscopy, chlorophyll fluorescence techniques, computer vision, multispectral and hyperspectral imaging, structured-illumination reflectance imaging, laser-induced backscattering imaging, optical coherence tomography, nuclear magnetic resonance and imaging, X-ray imaging, electronic nose, thermography, and acoustic methods, are summarized. We briefly introduce the principles of these techniques, summarize their applicability. The challenges and future trends are also proposed to provide beneficial reference for future researches and real-world applications.

Non-invasive quantification of vitamin C, citric acid, and sugar in 'Valência' oranges using infrared spectroscopies

Near (NIR) and mid (MIR) infrared spectroscopies have been studied as potential methods for non-destructive analyses of the fresh fruits quality. In this study, vitamin C, citric acid, total and reducing sugar content in 'Valência' oranges were evaluated using NIR and MIR spectroscopy with multivariate analysis. The spectral data were used to build up prediction models based on PLS (Partial Least Squares) regression. For vitamin C and citric acid, both NIR (r = 0.72 and 0.77, respectively) and MIR (0.81 and 0.91, respectively) resulted in feasible models. For sugars determination the two techniques presented a strong correlation between the reference values and analytical signals, with low RMSEP and r > 0.70 (NIR: sucrose RMSEP = 12.2 and r = 0.75; glucose RMSEP = 6.77 and r = 0.82; fructose RMSEP = 5.07 and r = 0.81; total sugar RMSEP = 12.1 and r = 0.80; reducing sugar RMSEP = 20.32 and r = 0.82; MIR: sucrose RMSEP = 9.47 and r = 0.80; glucose RMSEP = 6.70 and r = 0.82; fructose RMSEP = 5.20 and r = 0.81; total sugar RMSEP = 11.72 and r = 0.81; reducing sugar RMSEP = 20.42 and r = 0.81). The models developed with MIR presented lower prediction error rates than those made with NIR. Therefore, infrared techniques show applicability to determine of orange quality parameters in a non-destructive way.

Impact of Process Parameters and Bulk Properties on Quality of Dried Hops

Hops are critical to the brewing industry. In commercial hop drying, a large bulk of hops is dried in multistage kilns for several hours. This affects the drying behavior and alters the amount and chemical composition of the hop oils. To understand these changes, hops of the var. Hallertauer Tradition were dried in bulks of 15, 25 and 35 kg/m² at 60 °C and 0.35 m/s. Additionally, bulks of 25 kg/m² were also dried at 65 °C and 0.45 m/s to assess the effect of change in temperature and velocity, respectively. The results obtained show that bulk weights significantly influence the drying behavior. Classification based on the cone size reveals 45.4% medium cones, 41.2% small cones and 8.6% large cones. The highest ΔE value of 6.3 and specific energy consumption (113,476 kJ/kgH2O) were observed for the 15 kg/m² bulk. Increasing the temperature from 60 °C to 65 °C increased the oil yield losses by about 7% and myrcene losses by 22%. The results obtained show that it is important to define and consider optimum bulk and process parameters, to optimize the hop drying process to improve the process efficiency as well the product quality.

A Portable Spectrometric System for Quantitative Prediction of the Soluble Solids Content of Apples with a Pre-calibrated Multispectral Sensor Chipset

A portable spectrometric system for nondestructive assessment of the soluble solids content (SSC) of fruits for practical applications has been proposed and its performance has been examined by an experiment on quantitative prediction of the SSC of apples. Although the spectroscopic technique is a powerful tool for predicting the internal qualities of fruits, its practical applications are limited due to its high cost and complexity. In the proposed system, the spectra of apples were collected by a simple optical setup with a cheap pre-calibrated multispectral chipset. An optimal multiple linear regression model with five wavebands at 900, 760, 730, 680, and 535 nm revealed the best performance with the coefficient of determination of prediction and the root mean square error of prediction of 0.861 and 0.403 °Brix, respectively, which was comparable to that of the previous studies using dispersive spectrometers. Compared with previously reported systems using discrete filters or light emitting diodes, the proposed system was superior in terms of manufacturability and reproducibility. The experimental results confirmed that the proposed system had a considerable potential for practical, cost-effective applications of the SSC prediction, not only for apples but also for other fruits.

The combined use of GC, PDSC and FT-IR techniques to characterize fat extracted from commercial complete dry pet food for adult cats

This study aims to compare the quality of fat extracted from different priced dry pet food for adult cats through classical and instrumental methods: pressure differential scanning calorimetry (PDSC), Fourier transform infrared spectroscopy (FT-IR) or gas chromatography (GC). Fat extracted from pet food was examined for induction time (IT), fatty acid composition, free fatty acid (FFA) content and peroxide value with the use of PDSC, GC, acid–base and iodometric titration, respectively. FT-IR data from the selected spectral regions correlate with the value of oxidation IT or the content of FFA. This resulted in construction of a reference model for IT with the following statistical features: Rcalibration = 0.917 (RMSEC = 28.0) and Rvalidation = 0.841 (RMSEP = 34.6). For fatty acid content, model statistics were as follows: Rcalibration = 0.912 (RMSEC = 0.61) and Rvalidation = 0.856 (RMSEP = 0.75). Discriminant model that uses spectral data alone, calculated with performance index 83.7 allowed distinguishing the studied pet food samples due to the price. Studies conducted proved PDSC and IR as reliable analytical techniques to control and monitor the quality of dry pet food for cats. Considering quality of the studied samples, it was proved that low-priced pet foods can be stored longer than premium-priced ones, while former is nutritionally more beneficial for adult cats.

Applications of Cytokinins in Horticultural Fruit Crops: Trends and Future Prospects

Cytokinins (CKs) are a chemically diverse class of plant growth regulators, exhibiting wide-ranging actions on plant growth and development, hence their exploitation in agriculture for crop improvement and management. Their coordinated regulatory effects and cross-talk interactions with other phytohormones and signaling networks are highly sophisticated, eliciting and controlling varied biological processes at the cellular to organismal levels. In this review, we briefly introduce the mode of action and general molecular biological effects of naturally occurring CKs before highlighting the great variability in the response of fruit crops to CK-based innovations. We present a comprehensive compilation of research linked to the application of CKs in non-model crop species in different phases of fruit production and management. By doing so, it is clear that the effects of CKs on fruit set, development, maturation, and ripening are not necessarily generic, even for cultivars within the same species, illustrating the magnitude of yet unknown intricate biochemical and genetic mechanisms regulating these processes in different fruit crops. Current approaches using genomic-to-metabolomic analysis are providing new insights into the in planta mechanisms of CKs, pinpointing the underlying CK-derived actions that may serve as potential targets for improving crop-specific traits and the development of new solutions for the preharvest and postharvest management of fruit crops. Where information is available, CK molecular biology is discussed in the context of its present and future implications in the applications of CKs to fruits of horticultural significance.

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

The development of precise and reliable near infrared spectroscopy (NIRS)-based non-destructive tools to assess physicochemical properties of fleshy fruit has been challenging. A novel crop load × fruit developmental stage protocol for multivariate NIRS-based prediction models calibration to non-destructively assess peach internal quality and maturity was followed. Regression statistics of the prediction models highlighted that dry matter content (DMC, R² = 0.98, RMSEP = 0.41%), soluble solids concentration (SSC, R² = 0.96, RMSEP = 0.58%) and index of absorbance difference (I_AD, R² = 0.96, RMSEP = 0.08) could be estimated accurately with a single scan during fruit growth and development. Thus, the impact of preharvest factors such as crop load and canopy position on peach quality and maturity was evaluated. Large-scale field validation showed that heavier crop loads reduced peach quality (DMC, SSC) and delayed maturity (I_AD) and upper canopy position advanced both mainly in the moderate crop loads. This calibration protocol can enhance NIRS adaptation across tree fruit supply chain.

Non-Destructive Technologies for Detecting Insect Infestation in Fruits and Vegetables under Postharvest Conditions: A Critical Review

In the last two decades, food scientists have attempted to develop new technologies that can improve the detection of insect infestation in fruits and vegetables under postharvest conditions using a multitude of non-destructive technologies. While consumers' expectations for higher nutritive and sensorial value of fresh produce has increased over time, they have also become more critical on using insecticides or synthetic chemicals to preserve food quality from insects' attacks or enhance the quality attributes of minimally processed fresh produce. In addition, the increasingly stringent quarantine measures by regulatory agencies for commercial import-export of fresh produce needs more reliable technologies for quickly detecting insect infestation in fruits and vegetables before their commercialization. For these reasons, the food industry investigates alternative and non-destructive means to improve food quality. Several studies have been conducted on the development of rapid, accurate, and reliable insect infestation monitoring systems to replace invasive and subjective methods that are often inefficient. There are still major limitations to the effective in-field, as well as postharvest on-line, monitoring applications. This review presents a general overview of current non-destructive techniques for the detection of insect damage in fruits and vegetables and discusses basic principles and applications. The paper also elaborates on the specific post-harvest fruit infestation detection methods, which include principles, protocols, specific application examples, merits, and limitations. The methods reviewed include those based on spectroscopy, imaging, acoustic sensing, and chemical interactions, with greater emphasis on the noninvasive methods. This review also discusses the current research gaps as well as the future research directions for non-destructive methods' application in the detection and classification of insect infestation in fruits and vegetables.

Rapid detection of quality of Japanese fermented soy sauce using near-infrared spectroscopy

This study investigated the feasibility of rapidly evaluating the final quality of Japanese fermented soy sauce (shoyu) using NIR spectroscopy and partial least-squares (PLS) regression. In total, 110 shoyu samples that had been entered in the annual soy sauce competition from 2016 to 2018 were collected and analyzed. The transmittance spectra (400-1800 nm) and the transflectance spectra (680-2500 nm) of these samples were acquired and processed by different pre-treatments. PLS regression was applied to the raw and processed spectra to construct models based on a calibration set (76 shoyu samples from 2016 and 2017) and to evaluate these models using a validation set (34 shoyu samples from 2018), according to their values for bias and root mean square error of prediction (RMSEP). The results showed that the models constructed using the full spectra of transflectance performed better than those using transmittance spectra. Comparing the influence of different regions in the transflectance spectra enabled the accuracy of the models to be improved. The model constructed from transflectance spectra from the 1800 to 2500 nm region using pre-treatment of second derivative was superior to the other models, with a bias value of -2 and the lowest RMSEP value of 13 in the validation set. To further narrow the wavelength range, the models constructed using the spectral region from 2050 to 2400 nm also showed a better performance for predicting the sensory quality of soy sauce products. This study has demonstrated that the NIR spectroscopy technique could be used as an alternative routine quality control procedure, which can rapidly and economically classify the quality of soy sauce products.

Integration of Partial Least Squares Regression and Hyperspectral Data Processing for the Nondestructive Detection of the Scaling Rate of Carp (Cyprinus carpio)

The scaling rate of carp is one of the most important factors restricting the automation and intelligence level of carp processing. In order to solve the shortcomings of the commonly-used manual detection, this paper aimed to study the potential of hyperspectral technology (400-1024.7 nm) in detecting the scaling rate of carp. The whole fish body was divided into three regions (belly, back, and tail) for analysis because spectral responses are different for different regions. Different preprocessing methods, including Savitzky-Golay (SG), first derivative (FD), multivariate scattering correction (MSC), and standard normal variate (SNV) were applied for spectrum pretreatment. Then, the successive projections algorithm (SPA), regression coefficient (RC), and two-dimensional correlation spectroscopy (2D-COS) were applied for selecting characteristic wavelengths (CWs), respectively. The partial least square regression (PLSR) models for scaling rate detection using full wavelengths (FWs) and CWs were established. According to the modeling results, FD-RC-PLSR, SNV-SPA-PLSR, and SNV-RC-PLSR were determined to be the optimal models for predicting the scaling rate in the back (the coefficient of determination in calibration set (R_C²) = 96.23%, the coefficient of determination in prediction set (R_P²) = 95.55%, root mean square error by calibration (RMSEC) = 6.20%, the root mean square error by prediction (RMSEP)= 7.54%, and the relative percent deviation (RPD) = 3.98), belly (R_C² = 93.44%, R_P² = 90.81%, RMSEC = 8.05%, RMSEP = 9.13%, and RPD = 3.07) and tail (R_C² = 95.34%, R_P² = 93.71%, RMSEC = 6.66%, RMSEP = 8.37%, and RPD = 3.42) regions, respectively. It can be seen that PLSR integrated with specific pretreatment and dimension reduction methods had great potential for scaling rate detection in different carp regions. These results confirmed the possibility of using hyperspectral technology in nondestructive and convenient detection of the scaling rate of carp.

Application of the Non-Destructive NIR Technique for the Evaluation of Strawberry Fruits Quality Parameters

The determination of strawberry fruit quality through the traditional destructive lab techniques has some limitations related to the amplitude of the samples, the timing and the applicability along all phases of the supply chain. The aim of this study was to determine the main qualitative characteristics through traditional lab destructive techniques and Near Infrared Spectroscopy (NIR) in fruits of five strawberry genotypes. Principal Component Analysis (PCA) was applied to search for spectral differences among all the collected samples. A Partial Least Squares regression (PLS) technique was computed in order to predict the quality parameters of interest. The PLS model for the soluble solids content prediction was the best performing-in fact, it is a robust and reliable model and the validation values suggested possibilities for its use in quality applications. A suitable PLS model is also obtained for the firmness prediction-the validation values tend to worsen slightly but can still be accepted in screening applications. NIR spectroscopy represents an important alternative to destructive techniques, using the infrared region of the electromagnetic spectrum to investigate in a non-destructive way the chemical-physical properties of the samples, finding remarkable applications in the agro-food market.

Determination of the food dye indigotine in cream by near-infrared spectroscopy technology combined with random forest model

Artificial pigment is a common food additive in cream products. If added in excess, it will do harm to human body. At present, there is no research on the detection of cream pigment by Near Infrared (NIR) spectroscopy. In this paper, a method based on random forest was applied to determine the indigotine in cream. Weighting in the experiments was accomplished using analytical balances with precision as low as 0.0001 g. The NIR spectra data of cream with different concentration of indigotine were recorded. The original spectra was pretreated by SG smoothing, mean centering and second derivative. Random forest was applied to establish a quantitative analysis model for cream pigment content, and multiple evaluation criteria were selected to comprehensively evaluate the model. The R² was 0.9402, RMSEP was 0.2509 and RPD was 4.0893. Consequently, NIR spectroscopy, combined with data pretreatments and random forest model, was confirmed to be an interesting tool for non-destructive evaluation of pigment content in cream.

Broadband Time Domain Diffuse Optical Reflectance Spectroscopy: A Review of Systems, Methods, and Applications

This review presents recent developments and a wide overview of broadband time domain diffuse optical spectroscopy (TD-DOS). Various topics including physics of photon migration, advanced instrumentation, methods of analysis, applications covering multiple domains (tissue chromophore, in vivo studies, food, wood, pharmaceutical industry) are elaborated. The key role of standardization and recent studies in that direction are discussed. Towards the end, a brief outlook is presented on the current status and future trends in broadband TD-DOS.

Impact of Exclusion Netting Row Covers on 'Honeycrisp' Apple Trees Grown under Northeastern North American Conditions: Effects on Photosynthesis and Fruit Quality

Exclusion nets have been used successfully to protect fruit from insect pests of apples under various conditions, but the effect of each particular netting system on the plant itself has rarely been investigated. In this study, a complete exclusion system—in which the soil is also excluded—was used to grow ‘Honeycrisp’ apples for six years in southern Quebec, Canada. Abiotic conditions, as well as plant photosynthesis and fruit quality characteristics (colour, firmness, size, sugar content, number of seeds, ripeness and skin integrity) and yield were estimated yearly and compared in netted (either with or without a rainproof top) and unnetted row units. Although annual variations were high and results showed little or no difference between netted and unnetted rows for all measured variables, with the following exceptions; colour (increased red surface on fruits from unnetted rows some years), size (fruits from unnetted rows were smaller) and maturity (fruits from unnetted rows matured slightly faster). Fruits produced under nets had fewer microcracks at the surface than fruits produced without nets. Reduced cracking possibly helped decrease sooty blotch and flyspeck incidence and severity. Impacts for pest control and prospects for pesticide-free production are discussed.

Evaluation Performance of Ultrasonic Testing on Fruit Quality Determination

There are several destructive and nondestructive methods for quality evaluation of agricultural products. Most of the employed traditional techniques are time-consuming and involve considerable degree of manual works. Destructive methods provide reasonable success rate of quality determination of fruits; however, they practically have many concerns about effectiveness, time, and cost. Therefore, developing portable, fast, and cost-effective techniques without harming fruits are desired for fruit quality evaluation. This work aims to develop a complete nondestructive quality evaluation system with (a) ultrasonic testing and (b) volume estimation by automatic machine vision techniques. The ultrasonic system consisted of a programmable bipolar remote pulser unit, a couple of piezoelectric probes for ultrasonic signal acquisitions as a transmitter and a receiver, an oscilloscope, and a computer. Visual appearance (size/volume) was determined using a machine vision system based on image processing techniques. Five different images of a fruit from different angles were captured by high-resolution digital cameras. Volume of the fruit was computed after horizontal and vertical distance of the fruit’s images captured. The calculated volume values by the computer vision system are validated with the theoretical values. Although nondestructive ultrasonic estimation and volume estimation by image processing methods are cheap, fast, and practical, the results obtained in our experiments concluded that these methods are not as reliable as claimed in the literature.

Impact of drying methods on the changes of fruit microstructure unveiled by X-ray micro-computed tomography

Distinct evolution of fruit microstructure under different drying conditions were identified using a 3D imaging and Eulerian–Lagrangian analysis.

Data Fusion of Two Hyperspectral Imaging Systems with Complementary Spectral Sensing Ranges for Blueberry Bruising Detection

Currently, the detection of blueberry internal bruising focuses mostly on single hyperspectral imaging (HSI) systems. Attempts to fuse different HSI systems with complementary spectral ranges are still lacking. A push broom based HSI system and a liquid crystal tunable filter (LCTF) based HSI system with different sensing ranges and detectors were investigated to jointly detect blueberry internal bruising in the lab. The mean reflectance spectrum of each berry sample was extracted from the data obtained by two HSI systems respectively. The spectral data from the two spectroscopic techniques were analyzed separately using feature selection method, partial least squares-discriminant analysis (PLS-DA), and support vector machine (SVM), and then fused with three data fusion strategies at the data level, feature level, and decision level. The three data fusion strategies achieved better classification results than using each HSI system alone. The decision level fusion integrating classification results from the two instruments with selected relevant features achieved more promising results, suggesting that the two HSI systems with complementary spectral ranges, combined with feature selection and data fusion strategies, could be used synergistically to improve blueberry internal bruising detection. This study was the first step in demonstrating the feasibility of the fusion of two HSI systems with complementary spectral ranges for detecting blueberry bruising, which could lead to a multispectral imaging system with a few selected wavelengths and an appropriate detector for bruising detection on the packing line.

Application of Natural Frequencies for Prediction of Apple Texture Based on Partial Least Squares Regression

Experimental modal analysis was performed to identify natural frequencies to predict the texture of inhomogeneous tissues of apple (Malus domestinacv. ‘Golden Delicious’). Partial least squares calibration models based on natural frequencies with or without weight and density were created for predicting apple texture representing by yield gradient and initial modulus. The prediction models shown good prediction ability for texture of skin but impossible for flesh (all determination coefficients for skin models were more than 0.5 while for flesh models less than 0.5). A nondestructive and rapid method was provided to evaluate the fruit texture.