Prediction of banana color and firmness using a novel wavelengths selection method of hyperspectral imaging

Evaluation of Diffuse Reflectance Spectroscopy Vegetal Phantoms for Human Pigmented Skin Lesions

Pigmented skin lesions have increased considerably worldwide in the last years, with melanoma being responsible for 75% of deaths and low survival rates. The development and refining of more efficient non-invasive optical techniques such as diffuse reflectance spectroscopy (DRS) is crucial for the diagnosis of melanoma skin cancer. The development of novel diagnostic approaches requires a sufficient number of test samples. Hence, the similarities between banana brown spots (BBSs) and human skin pigmented lesions (HSPLs) could be exploited by employing the former as an optical phantom for validating these techniques. This work analyses the potential similarity of BBSs to HSPLs of volunteers with different skin phototypes by means of several characteristics, such as symmetry, color RGB tonality, and principal component analysis (PCA) of spectra. The findings demonstrate a notable resemblance between the attributes concerning spectrum, area, and color of HSPLs and BBSs at specific ripening stages. Furthermore, the spectral similarity is increased when a fiber-optic probe with a shorter distance (240 µm) between the source fiber and the detector fiber is utilized, in comparison to a probe with a greater distance (2500 µm) for this parameter. A Monte Carlo simulation of sampling volume was used to clarify spectral similarities.

Growth period determination and color coordinates visual analysis of tomato using hyperspectral imaging technology

Growth period determination and color coordinates prediction are essential for comparing postharvest fruit quality. This paper proposes a tomato growth period judgment and color coordinates prediction model based on hyperspectral imaging technology. It utilizes the most effective color coordinates prediction model to obtain a color visual image. Firstly, hyperspectral images were taken of tomatoes at different growth periods (green-ripe, color-changing, half-ripe, and full-ripe), and color coordinates (L*, a*, b*, c, h) were obtained using a colorimeter. The sample set was divided by the sample set partitioning based on joint X-Y distances (SPXY). The support vector machine (SVM), K-nearest neighbors (KNN), and linear discriminant analysis (LDA) were used to discriminate growth period. Results show that the LDA model has the best prediction effect with a prediction set accuracy of 93.1%. In addition, effective wavelengths were selected using competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA), and chromaticity prediction models were established using partial least squares regression (PLSR), multiple linear regression (MLR), principal component regression (PCR) and support vector machine regression (SVR) Finally, the color of each pixel of the tomato is calculated using the optimal model, generating a visual distribution image of the color coordinate. The results showed that hyperspectral imaging can non-destructively detect tomatoes' growth stage and color coordinates, providing great significance for designing a tomato quality grading system.

In-field and non-destructive determination of comprehensive maturity index and maturity stages of Camellia oleifera fruits using a portable hyperspectral imager

To efficiently detect the maturity stages of Camellia oleifera fruits, this study proposed a non-invasive method based on hyperspectral imaging technology. First, a portable hyperspectral imager was used for the in-field image acquisition of Camellia oleifera fruits at three maturity stages, and ten quality indexes were measured as reference standards. Then, factor analysis was performed to obtain the comprehensive maturity index (CMI) by analyzing the change trends and correlations of different indexes. To reduce the high dimensionality of spectral data, the successive projection algorithm (SPA) was employed to select effective feature wavelengths. The prediction models for CMI, including partial least squares regression (PLSR), support vector regression (SVR), extreme learning machine (ELM), and convolutional neural network regression (CNNR), were constructed based on full spectra and feature wavelengths; for CNNR, only the raw spectra were used as input. The SPA-CNNR model exhibited more promising performance (RP = 0.839, RMSEP = 0.261, and RPD = 1.849). Furthermore, PLS-DA models for maturity discrimination of Camellia oleifera fruits were developed using full wavelength, characteristic wavelengths and their fusion CMI, respectively. The PLS-DA model using the fused dataset achieved the highest maturity classification accuracy, with the best simplified model achieving 88.6 % accuracy in prediction set. This study indicated that a portable hyperspectral imager can be used for in-field determination of the internal quality and maturity stages of Camellia oleifera fruits. It provides strong support for non-destructive quality inspection and timely harvesting of Camellia oleifera fruits in the field.

Detection of wheat saccharification power and protein content using stacked models integrated with hyperspectral imaging

BACKGROUND

Wheat is one of the key ingredients used to make Chinese liquor, and its saccharification power and protein content directly affect the quality of the liquor. In pursuit of a non-destructive assessment of wheat components and the optimization of raw material proportions in liquor, this study introduces a precise predictive model that integrates hyperspectral imaging (HSI) with stacked ensemble learning (SEL).

RESULTS

This study extracted hyperspectral information from 14 different varieties of wheat and employed various algorithms for preprocessing. It was observed that multiplicative scatter correction (MSC) emerged as the most effective spectral preprocessing method. The feature wavelengths were extracted from the preprocessed spectral data using three different feature extraction methods. Then, single models (support vector machine (SVM), backpropagation neural network (BPNN), random forest (RF), and gradient boosting tree (XGBoost)) and a SEL model were developed to compare the prediction accuracies of the SEL model and the single models based on the full-band spectral data and the characteristic wavelengths. The findings indicate that the MSC-competitive adaptive reweighted sampling-SEL model demonstrated the highest prediction accuracy, with Rp 2 (test set-determined coefficient) values of 0.9308 and 0.9939 for predicting the saccharification power and protein content and root mean square error of the test set values of 0.0081 U and 0.0116 g kg-1, respectively.

CONCLUSION

The predictive model established in this study, integrating HSI and SEL models, accurately detected wheat saccharification power and protein content. This validation underscores the practical potential of the SEL model and holds significant importance for non-destructive component analysis of raw materials used in liquor. © 2024 Society of Chemical Industry.

Non-destructive prediction of TVB-N using color-texture features of UV-induced fluorescence image for freeze-thaw treated frozen-whole-round tilapia

BACKGROUND

The investigation of UV-induced fluorescence imaging coupled with machine learning was conducted to non-destructively detect the total volatile basic nitrogen (TVB-N) of frozen-whole-round tilapia (FWRT) during freezing and thawing. The UV-induced fluorescence images of FWRT at the wavelength of 365 nm were acquired by self-developed fluorescence image acquisition system. In total, 169 color and texture features based on RGB, hue-saturation-intensity and L*a*b* color spaces and gray level co-occurrence matrix were extracted, respectively. Successive projections algorithm (SPA) was employed to select the optimal 16 features to achieve feature dimension reduction modeling. With full and extracted features as input, the models of partial least squares regression (PLSR), least-squares support vector machine (LSSVM) and convolutional neural network (CNN) were established for TVB-N prediction.

RESULTS

Results indicated that the full features-based CNN performed better than SPA based prediction models (SPA-PLSR and SPA-LSSVM). The CNN model was determined to be the optimal with an RP2 value of 0.9779, RMSEP value of 1.1502 × 10-2  g N kg-1 and RPD value of 6.721 for TVB-N content predictiin.

CONCLUSION

The CNN method based on UV fluorescence imaging technology has potential for quality and safety detection of FWRT. © 2023 Society of Chemical Industry.

The Identification of Fritillaria Species Using Hyperspectral Imaging with Enhanced One-Dimensional Convolutional Neural Networks via Attention Mechanism

Combining deep learning and hyperspectral imaging (HSI) has proven to be an effective approach in the quality control of medicinal and edible plants. Nonetheless, hyperspectral data contains redundant information and highly correlated characteristic bands, which can adversely impact sample identification. To address this issue, we proposed an enhanced one-dimensional convolutional neural network (1DCNN) with an attention mechanism. Given an intermediate feature map, two attention modules are constructed along two separate dimensions, channel and spectral, and then combined to enhance relevant features and to suppress irrelevant ones. Validated by Fritillaria datasets, the results demonstrate that an attention-enhanced 1DCNN model outperforms several machine learning algorithms and shows consistent improvements over a vanilla 1DCNN. Notably under VNIR and SWIR lenses, the model obtained 98.97% and 99.35% for binary classification between Fritillariae Cirrhosae Bulbus (FCB) and other non-FCB species, respectively. Additionally, it still achieved an extraordinary accuracy of 97.64% and 98.39% for eight-category classification among Fritillaria species. This study demonstrated the application of HSI with artificial intelligence can serve as a reliable, efficient, and non-destructive quality control method for authenticating Fritillaria species. Moreover, our findings also illustrated the great potential of the attention mechanism in enhancing the performance of the vanilla 1DCNN method, providing reference for other HSI-related quality controls of plants with medicinal and edible uses.

A Review of Recent Advances for the Detection of Biological, Chemical, and Physical Hazards in Foodstuffs Using Spectral Imaging Techniques

Traditional methods for detecting foodstuff hazards are time-consuming, inefficient, and destructive. Spectral imaging techniques have been proven to overcome these disadvantages in detecting foodstuff hazards. Compared with traditional methods, spectral imaging could also increase the throughput and frequency of detection. This study reviewed the techniques used to detect biological, chemical, and physical hazards in foodstuffs including ultraviolet, visible and near-infrared (UV-Vis-NIR) spectroscopy, terahertz (THz) spectroscopy, hyperspectral imaging, and Raman spectroscopy. The advantages and disadvantages of these techniques were discussed and compared. The latest studies regarding machine learning algorithms for detecting foodstuff hazards were also summarized. It can be found that spectral imaging techniques are useful in the detection of foodstuff hazards. Thus, this review provides updated information regarding the spectral imaging techniques that can be used by food industries and as a foundation for further studies.

Prediction of acrylamide content in potato chips using near-infrared spectroscopy

Acrylamide (ACR), a neurotoxin with carcinogenic properties that can affect fertility, is commonly found in fried and baked foods such as potato chips. This study was carried out to predict the ACR content in fried and baked potato chips using near-infrared (NIR) spectroscopy. Effective wavenumbers were identified using competitive adaptive reweighted sampling (CARS) and the successive projections algorithm (SPA). Six wavenumbers (12799 cm^-1, 12007 cm^-1, 10944 cm^-1, 10943 cm^-1, 5801 cm^-1, and 4332 cm^-1) were selected using the ratio (λ_i/λ_j) and difference (λ_i-λ_j) of any two wavenumbers from the CARS and SPA results. First, partial least squares (PLS) models were established based on full spectral wavebands (12799-4000 cm^-1), and the prediction models were subsequently redeveloped based on effective wavenumbers to predict ACR content. Results showed that the full and selected wavenumbers-based PLS models obtained the coefficient of determination (R²) of 0.7707 and 0.6670, respectively, and the root mean square errors of prediction (RMSEP) of 53.0442 μg/kg and 64.3810 μg/kg, respectively, in the prediction sets. The results of this work demonstrate the suitability of NIR spectroscopy as a non-destructive method for predicting ACR content in potato chips.

Non-Destructive Banana Ripeness Detection Using Shallow and Deep Learning: A Systematic Review

The ripeness of bananas is the most significant factor affecting nutrient composition and demand. Conventionally, cutting and ripeness analysis requires expert knowledge and substantial human intervention, and different studies have been conducted to automate and substantially reduce human effort. Using the Preferred Reporting Items for the Systematic Reviews approach, 1548 studies were extracted from journals and conferences, using different research databases, and 35 were included in the final review for key parameters. These studies suggest the dominance of banana fingers as input data, a sensor camera as the preferred capturing device, and appropriate features, such as color, that can provide better detection. Among six stages of ripeness, the studies employing the four mentioned stages performed better in terms of accuracy and coefficient of determination value. Among all the works for detecting ripeness stages prediction, convolutional neural networks were found to perform sufficiently well with large datasets, whereas conventional artificial neural networks and support vector machines attained better performance for sensor-related data. However, insufficient information on the dataset and capturing device, limited data availability, and exploitation of data augmentation techniques are limitations in existing studies. Thus, effectively addressing these shortcomings and close collaboration with experts to predict the ripeness stages should be pursued.

Advances in Machine Learning and Hyperspectral Imaging in the Food Supply Chain

Food quality and safety are the essential hot issues of social concern. In recent years, there has been a growing demand for real-time food information, and non-destructive testing is gradually replacing traditional manual sensory testing and chemical analysis methods with lagging and destructive effects and has strong potential for application in the food supply chain. With the maturity and development of computer science and spectroscopic techniques, machine learning and hyperspectral imaging (HSI) have been widely demonstrated as efficient detection techniques that can be applied to rapidly evaluate sensory characteristics and quality attributes of food products nondestructively and efficiently. This paper first briefly described the basic concepts of hyperspectral imaging and machine learning, including the imaging process of HSI, the type of algorithms contained in machine learning, and the data processing flow. Secondly, this paper provided an objective and comprehensive overview of the current applications of machine learning and HSI in the food supply chain for sorting, packaging, transportation, storage, and sales, based on the state-of-art literature from 2017 to 2022. Finally, the potential of the technology is further discussed to provide optimized ideas for practical application.

Prediction of banana maturity based on the sweetness and color values of different segments during ripening

To predict the maturity of bananas, the present study used non-destructive methods to analyze changes in the sweetness and color of the stalks, middles, and tips of bananas during ripening. The results indicated that the respective maturation of these three segments did not occur simultaneously, as indicated by the differential enzyme activity and gene expression levels recorded in these segments. A principal component analysis and cluster plots were used to review the classification of banana maturity, highlighting that banana maturation can be divided into six stages. Two distinct maturity prediction algorithms were established using random forest, artificial neural network, and support vector machines, and they also indicated that dividing the maturity of bananas into six stages was adequate. These findings contribute to the development of quality evaluation and of a rapid grading system for processing, which improves the quality and sale of banana fruits and the related processed products.

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Sweetness and color during ripening were assessed along banana fingers.

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A new maturity prediction model was established for bananas.

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Banana maturity was divided in six stages.

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The theoretical basis for developing a maturity grading detection device was set.

Reflectance Spectroscopy with Multivariate Methods for Non-Destructive Discrimination of Edible Oil Adulteration

The visible and near-infrared (Vis-NIR) reflectance spectroscopy was utilized for the rapid and nondestructive discrimination of edible oil adulteration. In total, 110 samples of sesame oil and rapeseed oil adulterated with soybean oil in different levels were produced to obtain the reflectance spectra of 350–2500 nm. A set of multivariant methods was applied to identify adulteration types and adulteration rates. In the qualitative analysis of adulteration type, the support vector machine (SVM) method yielded high overall accuracy with multiple spectra pretreatments. In the quantitative analysis of adulteration rate, the random forest (RF) combined with multivariate scattering correction (MSC) achieved the highest identification accuracy of adulteration rate with the full wavelengths of Vis-NIR spectra. The effective wavelengths of the Vis-NIR spectra were screened to improve the robustness of the multivariant methods. The analysis results suggested that the competitive adaptive reweighted sampling (CARS) was helpful for removing the redundant information from the spectral data and improving the prediction accuracy. The PLSR + MSC + CARS model achieved the best prediction performance in the two adulteration cases of sesame oil and rapeseed oil. The coefficient of determination (RPcv2) and the root mean square error (RMSEPcv) of the prediction set were 0.99656 and 0.01832 in sesame oil adulterated with soybean oil, and the RPcv2 and RMSEPcv were 0.99675 and 0.01685 in rapeseed oil adulterated with soybean oil, respectively. The Vis-NIR reflectance spectroscopy with the assistance of multivariant analysis can effectively discriminate the different adulteration rates of edible oils.

Vis-NIR Hyperspectral Imaging for Online Quality Evaluation during Food Processing: A Case Study of Hot Air Drying of Purple-Speckled Cocoyam (Colocasia esculenta (L.) Schott)

In this study, hyperspectral imaging (HSI) and chemometrics were implemented to develop prediction models for moisture, colour, chemical and structural attributes of purple-speckled cocoyam slices subjected to hot-air drying. Since HSI systems are costly and computationally demanding, the selection of a narrow band of wavelengths can enable the utilisation of simpler multispectral systems. In this study, 19 optimal wavelengths in the spectral range 400–1700 nm were selected using PLS-BETA and PLS-VIP feature selection methods. Prediction models for the studied quality attributes were developed from the 19 wavelengths. Excellent prediction performance (RMSEP < 2.0, r2P > 0.90, RPDP > 3.5) was obtained for MC, RR, VS and aw. Good prediction performance (RMSEP < 8.0, r2P = 0.70–0.90, RPDP > 2.0) was obtained for PC, BI, CIELAB b*, chroma, TFC, TAA and hue angle. Additionally, PPA and WI were also predicted successfully. An assessment of the agreement between predictions from the non-invasive hyperspectral imaging technique and experimental results from the routine laboratory methods established the potential of the HSI technique to replace or be used interchangeably with laboratory measurements. Additionally, a comparison of full-spectrum model results and the reduced models demonstrated the potential replacement of HSI with simpler imaging systems.

Application of long-wave near infrared hyperspectral imaging for determination of moisture content of single maize seed

Moisture content (MC) is one of the most important factors for assessment of seed quality. However, the accurate detection of MC in single seed is very difficult. In this study, single maize seed was used as research object. A long-wave near infrared (LWNIR) hyperspectral imaging system was developed for acquiring reflectance images of the embryo and endosperm side of maize seed in the spectral range of 930-2548 nm, and the mixed spectra were extracted from both side of maize seeds. Then, Full-spectrum models were established and compared based on different types of spectra. It showed that models established based on spectra of the embryo side and mixed spectra obtained better performance than the endosperm side. Next, a combination of competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) was proposed to select the most effective wavelengths from full-spectrum data. In order to explore the stableness of wavelength selection algorithm, these methods were used for 200 independent experiments based on embryo side and mixed spectra, respectively. Each selection result was used as input of partial least squares regression (PLSR) and least squares support vector machine (LS-SVM) to build calibration models for determining the MC of single maize seed. Results indicated that the CARS-SPA-LS-SVM model established with mixed spectra was optimal for MC prediction in all models by considering the accuracy, stableness and complexity of models. The prediction accuracy of CARS-SPA-LS-SVM model is R_pre = 0.9311 ± 0.0094 and RMSEP = 1.2131 ± 0.0702 in 200 independent assessment. The overall study revealed that the long-wave near infrared hyperspectral imaging can be used to non-invasively and fast measure the MC in single maize seed and a robust and accurate model could be established based on CARS-SPA-LS-SVM method coupled with mixed spectral. These results can provide a useful reference for assessment of other internal quality attributes (such as starch content) of single maize seed.

Effect of dielectric barrier discharge cold plasma treatments on flavor fingerprints of brown rice

A non-thermal processing method was developed to promote preservation of brown rice using dielectric barrier discharge cold plasma (DBD-CP). Physicochemical properties including free fatty acid (FFA) content, surface color change, volatile organic components (VOCs) and flavor fingerprints were evaluated in brown rice submitted to DBD-CP. FFA levels were 25.2% lower in treated samples compared to the control, and a more stable surface color was obtained at the end of the storage period. A total of 35 major VOCs could be detected in treated samples, and reduced levels of hexanal can be used as an indicator of DBD-CP treatment in brown rice during storage. Moreover, the flavor fingerprints in DBD-CP treated groups can be successfully distinguished through headspace gas chromatography ion mobility spectrometry. Collectively, application of DBD-CP treatment could be utilized as a feasible approach to promote stabilization of brown rice and preserve flavor during storage.

Nondestructive determination of soluble solids content and pH in red bayberry (Myrica rubra) based on color space

Color has strong relationship with food quality. In this paper, partial least square regression (PLSR) and least square-support vector machine (LS-SVM) models combined with six different color spaces (NRGB, CIELAB, CMY, HSI, I1I2I3, and YCbCr) were developed and compared to predict pH value and soluble solids content (SSC) in red bayberry. The results showed that PLSR and LS-SVM models coupled with color space could predict pH value in red bayberry (r = 0.93-0.96, RMSE = 0.09-0.12, MAE = 0.07-0.09, and MRE = 0.04-0.06). In addition, the minimum errors (RMSE = 0.09, MAE = 0.07, and MRE = 0.04) and maximum correlation coefficient value (r = 0.96) were found with the PLSR based on CMY, I1I2I3, and YCbCr color spaces. For predicting SSC, PLSR models based on CIELAB color space (r = 0.90, RMSE = 0.91, MAE = 0.69 and MRE = 0.12) and HSI color space (r = 0.89, RMSE = 0.95, MAE = 0.73 and MRE = 0.13) were recommended. The results indicated that color space combined with chemometric is suitable to non-destructively detect pH value and SSC of red bayberry.

Application of hyperspectral technology in detection of agricultural products and food: A Review

Food is the foundation of human survival. With the development and progress of society, people increasingly focus on the problems of food quality and safety, which is closely related to human's health. Thus, the whole industrial chain from farmland to dining table need to be strictly controlled. Traditional detection methods are time-consuming, laborious, and destructive. In recent years, hyperspectral technology has been more and more applied to food safety and quality detection, because the technology can achieve rapid and nondestructive detection of food, and the requirement to experimental condition is low; operability is strong. In this paper, hyperspectral imaging technology was briefly introduced, and its application in agricultural products and food detection in recent years was systematically summarized, and the key points in the research process were deeply discussed. This work lays a solid foundation for the peers to the following in-depth research and application of this technology.

Application of Hyperspectral Imaging as a Nondestructive Technique for Foodborne Pathogen Detection and Characterization

Microbial food safety is a persistent and exacting global issue due to the multiplicity and complexity of foods and food production systems. Foodborne illnesses caused by foodborne bacterial pathogens frequently occur, thus endangering the safety and health of human beings. Factors such as pretreatments, that is, culturing, enrichment, amplification make the traditional routine identification and enumeration of large numbers of bacteria in a complex microbial consortium complex, expensive, and time-consuming. Therefore, the need for rapid point-of-use detection systems for foodborne bacterial pathogens with high sensitivity and specificity is crucial in food safety control. Hyperspectral imaging (HSI) as a powerful testing technology provides a rapid, nondestructive approach for pathogen detection. This article reviews some fundamental information about HSI, including instrumentation, data acquisition, image processing, and data analysis-the current application of HSI for the detection, classification, and discrimination of various foodborne pathogens. The merits and demerits of HSI for pathogen detection as well as current and future trends are discussed. Therefore, the purpose of this review is to provide a brief overview of HSI, and further lay emphasis on the emerging trend and importance of this technique for foodborne pathogen detection.

Sample Consensus Model and Unsupervised Variable Consensus Model for Improving the Accuracy of a Calibration Model

In the quantitative analysis of spectral data, small sample size and high dimensionality of spectral variables often lead to poor accuracy of a calibration model. We proposed two methods, namely sample consensus and unsupervised variable consensus models, in order to solve the problem of poor accuracy. Three public near-infrared (NIR) or infrared (IR) spectroscopy data from corn, wine, and soil were used to build the partial least squares regression (PLSR) model. Then, Monte Carlo sampling and unsupervised variable clustering methods of a self-organizing map were coupled with the consensus modeling strategy to establish the multiple sub-models. Finally, sample consensus and unsupervised variable consensus models were obtained by assigning the weights to each PLSR sub-model. The calculated results show that both sample consensus and unsupervised variable consensus models can significantly improve the accuracy of the calibration model compared to the single PLSR model. The effectiveness of these two methods points out a new approach to achieve a further accurate result, which can take full advantage of the sample information and valid variable information.

Assessment of External Properties for Identifying Banana Fruit Maturity Stages Using Optical Imaging Techniques

The maturity stage of bananas has a considerable influence on the fruit postharvest quality and the shelf life. In this study, an optical imaging based method was formulated to assess the importance of different external properties on the identification of four successive banana maturity stages. External optical properties, including the peel color and the local textural and local shape information, were extracted from the stalk, middle and tip of the bananas. Specifically, the peel color attributes were calculated from individual channels in the hue-saturation-value (HSV), the International Commission on Illumination (CIE) L*a*b* and the CIE L*ch color spaces; the textural information was encoded using a local binary pattern with uniform patterns (UP-LBP); and the local shape features were described by histogram of oriented gradients (HOG). Three classifiers based on the naïve Bayes (NB), linear discriminant analysis (LDA) and support vector machine (SVM) algorithms were adopted to evaluate the performance of identifying banana fruit maturity stages using the different optical appearance features. The experimental results demonstrate that overall identification accuracies of 99.2%, 100% and 99.2% were achieved using color appearance features with the NB, LDA and SVM classifiers, respectively; overall accuracies of 92.6%, 86.8% and 93.4% were obtained using local textural features for the three classifiers, respectively; and overall accuracies of only 84.3%, 83.5% and 82.6% were obtained using local shape features with the three classifiers, respectively. Compared to the complicated calculation of both the local textural and local shape properties, the simplicity and high accuracy of the peel color property make it more appropriate for identifying banana fruit maturity stages using optical imaging techniques.

Health Benefits of Green Banana Consumption: A Systematic Review

Despite the growing demand for green banana (GB) products, there is no review study regarding their potential health benefits. We aimed to compare the health benefits among different GB products by a systematic review. We researched six electronic databases (PubMed, EMBASE, Scopus, Science Direct, Web of Science, and Google Scholar) from inception to March 2019. We found 1009 articles in these databases. After duplicate removal, we screened 732 articles' titles and abstracts, and selected 18 potentially relevant studies for full-text reading. We added five records from the reference list of the fully-read articles and seven suggested by the expert. Twelve articles were excluded. In the end, 18 studies were considered for this systematic review. Ten studies were conducted with green banana flour and eight with the green banana pulp/biomass. Most of the GB health benefits studied were related to the gastrointestinal symptoms/diseases, followed by the glycemic/insulin metabolism, weight control, and renal and liver complications associated to diabetes. Only one study did not confirm the health benefit proposed. It is necessary to standardize the GB dose/effect to different age groups and different health effects considering the GB variety and ripeness level. Further studies are necessary to present better detailing of GB product and their health effects considering all the raw-material characteristics.

Rapid-Detection Sensor for Rice Grain Moisture Based on NIR Spectroscopy

Rice grain moisture has a great impact on th production and storage storage quality of rice. The main objective of this study was to design and develop a rapid-detection sensor for rice grain moisture based on the Near-infrared spectroscopy (NIR) characteristic band, aiming to realize its accurate and on-line measurement. In this paper, the NIR spectral information of grain samples with different moisture content was obtained using a portable NIR spectrometer. Then, the partial least squares (PLS) and competitive adaptive reweighted squares (CARS) were applied to model and analyze the spectral data to find the rice grain moisture NIR spectroscopy. As a result, the 1450 nm band was sensitive to the rice grain moisture and a rapid-detection sensor was developed with a 1450 nm light emitting diode (LED) light source, InGaAs photodiode, lens and filter, whose basic principle is to establish the relationship between the rice grain moisture and the measured voltage signal. To evaluate the sensor performance, rice grain samples with 13–30% moisture content were detected, the coefficient of determination R2 was 0.936, and the sum of squares for error (SSE) was 23.44. It is concluded that this study provides a spectroscopic measuring method, as well as developing an effective and accurate sensor for the rapid determination of rice grain moisture, which is of great significance for monitoring the quality of rice grain during its production, transportation and storage process.

Rapid prediction of chlorophylls and carotenoids content in tea leaves under different levels of nitrogen application based on hyperspectral imaging

BACKGROUND

Photosynthetic pigments perform critical physiological functions in tea plants. Their content is an essential indicator of photosynthetic efficiency and nutritional status. The present study aimed to predict chlorophyll a (Chl a), chlorophyll b (Chl b), total chlorophyll (total Chl), and carotenoid (Car) content in tea leaves under different levels of nitrogen treatment using hyperspectral imaging (HSI) in combination with variable selection algorithms.

RESULTS

A total of 150 samples were collected and scanned using the HSI system. The mean spectrum in the region of interest (ROI) was extracted, and the pigment content was measured by traditional chemical methods. Five and seven optimal wavelengths (OWs) were selected using the regression coefficients (RCs) of partial least squares regression (PLSR) and the second-derivative (2-Der), respectively. The optimal 2-Der-PLSR models for Chl a, Chl b, total Chl, and Car performed remarkably well based on seven OWs with correlation coefficients of prediction (R_P ) of 0.9337, 0.9322, 0.9333 and 0.9036, root mean square errors in prediction (RMSEP) of 0.1100, 0.0511, 0.1620, and 0.0300 mg g^-1 , respectively.

CONCLUSION

The results of this study revealed that HSI combined with variable selection method can be employed as a rapid and accurate method for predicting the content of pigments in tea plants. © 2018 Society of Chemical Industry.

Non-Destructive and Rapid Variety Discrimination and Visualization of Single Grape Seed Using Near-Infrared Hyperspectral Imaging Technique and Multivariate Analysis

Hyperspectral images in the spectral range of 874⁻1734 nm were collected for 14,015, 14,300 and 15,042 grape seeds of three varieties, respectively. Pixel-wise spectra were preprocessed by wavelet transform, and then, spectra of each single grape seed were extracted. Principal component analysis (PCA) was conducted on the hyperspectral images. Scores for images of the first six principal components (PCs) were used to qualitatively recognize the patterns among different varieties. Loadings of the first six PCs were used to identify the effective wavelengths (EWs). Support vector machine (SVM) was used to build the discriminant model using the spectra based on the EWs. The results indicated that the variety of each single grape seed was accurately identified with a calibration accuracy of 94.3% and a prediction accuracy of 88.7%. An external validation image of each variety was used to evaluate the proposed model and to form the classification maps where each single grape seed was explicitly identified as belonging to a distinct variety. The overall results indicated that a hyperspectral imaging (HSI) technique combined with multivariate analysis could be used as an effective tool for non-destructive and rapid variety discrimination and visualization of grape seeds. The proposed method showed great potential for developing a multi-spectral imaging system for practical application in the future.

Nondestructive and rapid determination of lignocellulose components of biofuel pellet using online hyperspectral imaging system

BACKGROUND

In the pursuit of sources of energy, biofuel pellet is emerging as a promising resource because of its easy storage and transport, and lower pollution to the environment. The composition of biomass has important implication for energy conversion processing strategies. Current standard chemical methods for biomass composition are laborious, time-consuming, and unsuitable for high-throughput analysis. Therefore, a reliable and efficient method is needed for determining lignocellulose composition in biomass and so to accelerate biomass utilization. Here, near-infrared hyperspectral imaging (900-1700 nm) together with chemometrics was used to determine the lignocellulose components in different types of biofuel pellets. Partial least-squares regression and principal component multiple linear regression models based on whole wavelengths and optimal wavelengths were employed and compared for predicting lignocellulose composition.

RESULTS

Out of 216 wavelengths, 20, 10 and 17 were selected by the successive projections algorithm for cellulose, hemicellulose and lignin, respectively. Three simple and satisfactory prediction models were constructed, with coefficients of determination of 0.92, 0.84 and 0.71 for cellulose, hemicellulose and lignin, respectively. The relative parameter distributions were quantitatively visualized through prediction maps by transferring the optimal models to all pixels on the hyperspectral image.

CONCLUSIONS

Hence, the overall results indicated that hyperspectral imaging combined with chemometrics offers a non-destructive and low-cost method for determining biomass lignocellulose components, which would help in developing a simple multispectral imaging instrument for biofuel pellets online measurement and improving the production management.