Visible Near-infrared Hyperspectral Imaging Research Articles

Identification of Coal and Gangue Using Visible-Near Infrared (Vis-NIR) Hyperspectral Imaging

Accurate and rapid identification of coal and gangue is a crucial issue for efficient resource utilization and environmental protection. In this study, visible and near infrared (Vis-NIR) hyperspectral imaging was used for the classification of coal and gangue. Principal component analysis was adopted to generate score scatter plots for differentiating specific grouping of samples. Competitive adaptive reweighted sampling and random frog (RF) were employed to screen out the effective wavelengths. Three models, partial least squares, a library for support vector machines, and back-propagation (BP) neural network were developed to identify coal and gangue using effective wavelengths and full spectra as inputs. The results showed that all models provided satisfactory identification rates of over 91.6%. The best performances emerged from BP neural network using effective wavelengths extracted by RF with identification accuracy of 100% in validation sets. It can be concluded that Vis-NIR hyperspectral imaging is a noninvasive analytical method with promising application prospects for coal and gangue identification.

Advancing sweetpotato quality assessment with hyperspectral imaging and explainable artificial intelligence

The quality evaluation of sweetpotatoes is of utmost importance during postharvest handling as it significantly impacts consumer satisfaction, nutritional value, and market competitiveness. This study presents an innovative approach that integrates explainable artificial intelligence (AI) with hyperspectral imaging to enhance the assessment of three important quality attributes in sweetpotatoes, i.e., dry matter content, soluble solid content, and firmness. Sweetpotato samples of three different varieties, including “Bayou Belle”, “Murasaki”, and “Orleans”, were imaged using a portable visible near-infrared hyperspectral imaging (VNIR-HSI) camera, with a 400–1000 nm spectral range. The extracted spectral data were used to select key wavelengths, develop multivariate regression models, and utilize SHapley Additive exPlanations (SHAP) values to ascertain model effectiveness and interpretability. The regression models (dry matter: R2p = 0.92, RMSEP = 1.50 % and RPD = 5.58; soluble solid content: R2p = 0.66, RMSEP = 0.85obrix, and RPD = 1.72; firmness: R2p = 0.85; RMSEP = 1.66 N and RPD = 2.63) developed with key wavelengths were used to generate prediction maps to visualize the spatial distribution of response attributes, facilitating an improved evaluation of sweetpotato quality. The study demonstrated that the combination of HSI, variable selection, and explainable AI has the potential to enhance the quality assessment of sweetpotatoes, ensuring supplies of higher quality products to consumers.

Early Detection of Dicamba and 2,4-D Herbicide Drifting Injuries on Soybean with a New Spatial–Spectral Algorithm Based on LeafSpec, an Accurate Touch-Based Hyperspectral Leaf Scanner

In soybeans, off-target damage from the use of dicamba and 2,4-D herbicides for broadleaf weed control can significantly impact sensitive vegetation and crops. The early detection and assessment of such damage are critical for plant diagnostic labs and regulatory agencies to inform regulated usage policies. However, the existing technologies that calculate the average spectrum often struggle to detect and differentiate the damage caused by these herbicides, as they share a similar mode-of-action. In this study, a high-precision spatial and spectral imaging solution was tested for the early detection of dicamba and 2,4-D-induced damage in soybeans. A 2021 study was conducted using LeafSpec, a touch-based hyperspectral leaf scanner, to detect damage on soybean leaves. VIS-NIR (visible–near infrared) hyperspectral images were captured from 180 soybean plants exposed to nine different herbicide treatments at different intervals after spraying. Leaf damage was distinguished as early as 2 h after treatment (HAT) using pairwise partial least squares discriminant analysis (PLS-DA) models based on spectral data. Leaf color distribution, texture, and morphological features were analyzed to separate herbicide dosages. By fully exploiting the spatial and spectral information from high-resolution hyperspectral images, classification accuracy was improved from 57.4% to over 80% for all evaluation dates. This work demonstrates the potential and advantages of using spectral and spatial features of LeafSpec hyperspectral images for the early and accurate detection of herbicide damage in soybean plants.

Simultaneous Quantification and Visualization of Photosynthetic Pigments in Lycopersicon esculentum Mill. under Different Levels of Nitrogen Application with Visible-Near Infrared Hyperspectral Imaging Technology.

Leaf photosynthetic pigments play a crucial role in evaluating nutritional elements and physiological states. In facility agriculture, it is vital to rapidly and accurately obtain the pigment content and distribution of leaves to ensure precise water and fertilizer management. In our research, we utilized chlorophyll a (Chla), chlorophyll b (Chlb), total chlorophylls (Chls) and total carotenoids (Cars) as indicators to study the variations in the leaf positions of Lycopersicon esculentum Mill. Under 10 nitrogen concentration applications, a total of 2610 leaves (435 samples) were collected using visible-near infrared hyperspectral imaging (VNIR-HSI). In this study, a "coarse-fine" screening strategy was proposed using competitive adaptive reweighted sampling (CARS) and the iteratively retained informative variable (IRIV) algorithm to extract the characteristic wavelengths. Finally, simultaneous and quantitative models were established using partial least squares regression (PLSR). The CARS-IRIV-PLSR was used to create models to achieve a better prediction effect. The coefficient determination (R2), root mean square error (RMSE) and ratio performance deviation (RPD) were predicted to be 0.8240, 1.43 and 2.38 for Chla; 0.8391, 0.53 and 2.49 for Chlb; 0.7899, 2.24 and 2.18 for Chls; and 0.7577, 0.27 and 2.03 for Cars, respectively. The combination of these models with the pseudo-color image allowed for a visual inversion of the content and distribution of the pigment. These findings have important implications for guiding pigment distribution, nutrient diagnosis and fertilization decisions in plant growth management.

Comparison of a portable Vis-NIR hyperspectral imaging and a snapscan SWIR hyperspectral imaging for evaluation of meat authenticity.

The performance of visible-near infrared hyperspectral imaging (Vis-NIR-HSI) (400-1000nm) and shortwave infrared hyperspectral imaging (SWIR-HSI) (1116-1670nm) combined with different classification and regression (linear and non-linear) multivariate methods were assessed for meat authentication. In Vis-NIR-HSI, total accuracies in the prediction set for SVM and ANN-BPN (the best classification models) were 96 and 94% surpassing the performance of SWIR-HSI with 88 and 89% accuracy, respectively. In Vis-NIR-HSI, the best-obtained coefficient of determinations for the prediction set (R2p) were 0.99, 0.88, and 0.99 with root mean square error in prediction (RMSEP) of 9, 24 and 4 (%w/w) for pork in beef, pork in lamb and pork in chicken, respectively. In SWIR-HSI, the best-obtained R2p were 0.86, 0.77, and 0.89 with RMSEP of 16, 23 and 15 (%w/w) for pork in beef, pork in lamb and pork in chicken, respectively. The results ascertain that Vis-NIR-HSI coupled with multivariate data analysis has better performance rather than SWIR-HIS.

Visible-near-infrared hyperspectral imaging combined with ensemble learning for the nutrient content of Pinus elliottii × P. caribaea canopy needles detection

IntroductionPinus elliottii × P. caribaea is one of the major tree species in commercial forest bases in developed countries. However, in the process of sapling cultivation, nutrients cannot be accurately detected and supplied to individual saplings, resulting in reduced yield and quality.MethodsIn this paper, visible-near-infrared (Vis-NIR) hyperspectral imaging (HSI) combined with ensemble learning (EL) was used to solve this problem. The content and distribution of nitrogen (N), phosphorus (P), and potassium (K) in the canopy needles of Pinus elliottii × P. caribaea saplings were obtained through HSI data analysis, and the nutritional needs of individual plants were reflected to provide a basis for nutritional supply decisions. The saplings were treated with deficient, sufficient, and excessive N, P, and K single-element fertilizers. After collecting the Vis-NIR hyperspectral images of these saplings, a variety of pre-processing, feature selection, and ensemble learning algorithms were used to establish predictive models. The R2 and RMSE were used to evaluate the performance of the prediction models.ResultsThe results showed that the multiple scattering correction-competitive adaptive reweighted sampling-Stacking (MSC-CARS-Stacking) model had the best results among the three nutrient elements prediction models (Rp2-N = 0.833, RMSEP = 0.380; Rp2-P = 0.622, RMSEP = 0.101; Rp2-K = 0.697, RMSEP = 0.523). When studying the sensitive bands of N, P, and K, we found that the common characteristic wavelengths were 675.3 and 923.9 nm, while the non-common characteristic wavelengths were located at 550 nm (green peak), 680 nm (red valley), and 960 nm (water peak). In studying the generalization ability of the model, only the nitrogen group data were used to train the MSC-CARS-Stacking model for nitrogen prediction, which was then used to predict the nitrogen content in the phosphorus and potassium groups, obtaining good results (Rc2-N = 0.841, Rp2-P = 0.814, Rp2-K = 0.801). It showed a strong generalization ability for the prediction of nitrogen, and similarly, phosphorus and potassium.DiscussionIn conclusion, this study verifies that the Vis-NIR HSI combined with EL is indeed a reliable and stable method to predict the contents of N, P, and K in the needles of Pinus elliottii × P. caribaea sapling canopy.

Detecting different pesticide residues on Hami melon surface using hyperspectral imaging combined with 1D-CNN and information fusion.

Efficient, rapid, and non-destructive detection of pesticide residues in fruits and vegetables is essential for food safety. The visible/near infrared (VNIR) and short-wave infrared (SWIR) hyperspectral imaging (HSI) systems were used to detect different types of pesticide residues on the surface of Hami melon. Taking four pesticides commonly used in Hami melon as the object, the effectiveness of single-band spectral range and information fusion in the classification of different pesticides was compared. The results showed that the classification effect of pesticide residues was better by using the spectral range after information fusion. Then, a custom multi-branch one-dimensional convolutional neural network (1D-CNN) model with the attention mechanism was proposed and compared with the traditional machine learning classification model K-nearest neighbor (KNN) algorithm and random forest (RF). The traditional machine learning classification model accuracy of both models was over 80.00%. However, the classification results using the proposed 1D-CNN were more satisfactory. After the full spectrum data was fused, it was input into the 1D-CNN model, and its accuracy, precision, recall, and F1-score value were 94.00%, 94.06%, 94.00%, and 0.9396, respectively. This study showed that both VNIR and SWIR hyperspectral imaging combined with a classification model could non-destructively detect different pesticide residues on the surface of Hami melon. The classification result using the SWIR spectrum was better than that using the VNIR spectrum, and the classification result using the information fusion spectrum was better than that using SWIR. This study can provide a valuable reference for the non-destructive detection of pesticide residues on the surface of other large, thick-skinned fruits.

Prediction of soil organic carbon in soil profiles based on visible–near-infrared hyperspectral imaging spectroscopy

Complete and coherent soil profile information is important for studying soil morphological characteristics, vertical variation patterns of soil physicochemical properties, and identifying soil types. However, traditional soil survey methods use a limited number of discrete sample points at different depths as a data source to explore the correlation between soil depth and soil properties. Due to the variability of soil properties at vertical depth, there is a strong uncertainty in the conclusions drawn from the limited sample points. With the development of near-Earth remote sensing technology, the near-Earth hyperspectral imaging system can acquire hyperspectral images of soil profiles and characterize the fine texture differences and spectral variations of soil with continuous spectral curves and images, which is useful for inversion of soil properties. Soil organic carbon (SOC) is one of the important physicochemical properties to identify the genetic horizon, therefore, this paper explored the ability of visible–near-infrared (Vis-NIR) hyperspectral imaging spectroscopy to predict SOC in soil monoliths. The dataset contained five soil monoliths retrieved from the upper Yellow River, China. A random forest (RF) model was developed for predicting organic carbon, and the organic carbon content in the genetic horizon was used to establish a depth function to obtain the continuous content variation with the depth. These differences with and without depth function modeling, and the effect of three equal-spacing sampling schemes (1, 5, and 10 cm) on the depth function and the accuracy differences in five spectral statistics of the maximum (max), mean, median, mode, and minimum (min) within the image region of interest (ROI) were compared. The results indicated that (1) the depth function under the 1-cm sampling scheme predicted organic carbon the best, (2) the mode of the spectra within the ROI could yield a comparable or even a higher accuracy than that obtained with the mean, and (3) imaging spectroscopy could be used to visualize the distribution of organic carbon on soil profiles with relatively uniform morphology. These results indicated that imaging spectroscopy could be used for predicting organic carbon in undisturbed soil profiles.

Non-destructive prediction and visualization of anthocyanin content in mulberry fruits using hyperspectral imaging.

Being rich in anthocyanin is one of the most important physiological traits of mulberry fruits. Efficient and non-destructive detection of anthocyanin content and distribution in fruits is important for the breeding, cultivation, harvesting and selling of them. This study aims at building a fast, non-destructive, and high-precision method for detecting and visualizing anthocyanin content of mulberry fruit by using hyperspectral imaging. Visible near-infrared hyperspectral images of the fruits of two varieties at three maturity stages are collected. Successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS) and stacked auto-encoder (SAE) are used to reduce the dimension of high-dimensional hyperspectral data. The least squares-support vector machine and extreme learning machine (ELM) are used to build models for predicting the anthocyanin content of mulberry fruit. And genetic algorithm (GA) is used to optimize the major parameters of models. The results show that the higher the anthocyanin content is, the lower the spectral reflectance is. 15, 7 and 13 characteristic variables are extracted by applying CARS, SPA and SAE respectively. The model based on SAE-GA-ELM achieved the best performance with R2 of 0.97 and the RMSE of 0.22 mg/g in both the training set and testing set, and it is applied to retrieve the distribution of anthocyanin content in mulberry fruits. By applying SAE-GA-ELM model to each pixel of the mulberry fruit images, distribution maps are created to visualize the changes in anthocyanin content of mulberry fruits at three maturity stages. The overall results indicate that hyperspectral imaging, in combination with SAE-GA-ELM, can help achieve rapid, non-destructive and high-precision detection and visualization of anthocyanin content in mulberry fruits.

Non-destructive discrimination of homochromatic foreign materials in cut tobacco based on VIS-NIR hyperspectral imaging.

The presence of foreign materials (FM) not only reduces the commercial value of tobacco and the quality of cigarette products, but also affects the aroma and flavor of cigarettes. Existing tobacco deblending equipment has received little study with respect to homochromatic FM. In the present study, visible-near infrared (VIS-NIR) hyperspectral imaging technique combined with chemometrics were used to identify and visualize the homochromatic FM on the surface of thining tobacco. A comparison with conventional vision method was made to analyze the feasibility of the method. The importance of detecting FM in cut tobacco was further demonstrated by first studying the volatile organic compounds produced in cigarette mixed FM smoke and their effects on human health before conducting hyperspectral experiments. The results indicated that solid-phase microextraction and gas chromatography mass spectrometry could detect volatile organic compounds in mainstream cigarette smoke that were not cigarette components and affected consumer health. Then, spectral features of the samples were extracted from hyperspectral images for building identification models to distinguish FM from cut tobacco. The visual RGB values of cut tobacco and FM were also used for the analysis of the recognition models. The results showed that the accuracy, precision and recall reached 100.00% using the back propagation artificial neural network classification model based on the principal component analysis raw wavelengths. The visualization results based on the optimal model produced clearer localization than conventional computer vision method. The present study revealed that the VIS-NIR hyperspectral imaging technology had advantage in the detection and localization of FM on the surface of thinning tobacco, which provided a foundation for improving the quality and safety of cut tobacco production. © 2023 Society of Chemical Industry.

Combining Vis-NIR and NIR hyperspectral imaging techniques with a data fusion strategy for the rapid qualitative evaluation of multiple qualities in chicken

Total volatile basic nitrogen (TVB-N) and total viable count (TVC) are two important indicators for evaluating and controlling the quality and safety of chicken fillets. The aim of this study is to evaluate two hyperspectral imaging techniques (visible near-infrared (Vis-NIR) and NIR) in combination with low-level and intermediate-level fusion strategies (LLF and ILF) for the prediction of multiple quality indicators of chicken fillets stored at 4 °C. Quantitative predictions using partial least squares regression (PLSR) were established after preprocessing and feature wavelength selection. The results showed that the data fusion strategy exhibited better performance in both indicators compared to individual data. The LLF strategy showed optimal performance in predicting the TVC content with an RP2 of 0.9275 and an RMSEP of 0.1889, while the ILF strategy was best in predicting the TVB-N content with an RP2 of 0.8652 and an RMSEP of 2.6094. Moreover, the optimal models based on selected bands were used to achieve visual maps of the TVB-N and TVC contents. Although validation with an independent batch of samples was not used, it was a feasibility and valuable study. The experiment results demonstrated that the fusion of two types of hyperspectral data can be successfully used to evaluation of chicken multiple qualities, and provided a potential method for monitoring and detection of meat qualities.

Feasibility of Visible Near-Infrared Hyperspectral Imaging in Detection of Calcium Hypochlorite in Sago Flour

The general public perspective on sago flour quality is based on the perceived colour appearances. This contributed to the potential of food fraud by excessive usage of bleaching agents such as calcium hypochlorite (CHC) to alter the product’s colour. Conventional methods to detect and quantify CHC such as titration and chromatography are time-consuming, expensive and limited to laboratory setups only. In this research, visible near-infrared hyperspectral imaging (Vis-NIR HSI) was combined with partial least squares regression (PLSR) model to quantify CHC in pure sago flour accurately and rapidly. Hyperspectral images with the spectral region of 400 nm to 1000 nm were captured for CHC-pure sago mixture samples with CHC concentration ranging from 0.005 w/w% to 2 w/w%. Mean reflectance spectral data was extracted from the hyperspectral images, and was used as inputs to develop the PLSR model to predict the CHC concentration. The PLSR model achieved the commendable predictive results in this study, with Rp = 0.9509, RMSEP = 0.1655 and MAPEP of 3.801%, proving that Vis-NIR HSI can effectively predict the concentration of CHC in sago flour.

Global calibration for non-targeted fraud detection in quinoa flour using portable hyperspectral imaging and chemometrics

Quinoa is one of the highest nutritious grains, and global consumption of quinoa flour has increased as people pay more attention to health. Due to its high value, quinoa flour is susceptible to adulteration. Cross-contamination between quinoa flour and other flour can be easily neglected due to their highly similar appearance. Therefore, detecting adulteration in quinoa flour is important to consumers, industries, and regulatory agencies. In this study, portable hyperspectral imaging in the visible near-infrared (VNIR) spectral range (400–1000 nm) was applied as a rapid tool to detect adulteration in quinoa flour. Quinoa flour was adulterated with wheat, rice, soybean, and corn in the range of 0–98% with 2% increments. Partial least squares regression (PLSR) models were developed, and the best model for detecting the % authentic flour (quinoa) was obtained by the raw spectral data with R2p of 0.99, RMSEP of 3.08%, RPD of 8.77, and RER of 25.32. The model was improved, by selecting only 13 wavelengths using bootstrapping soft shrinkage (BOSS), to R2p of 0.99, RMSEP of 2.93%, RPD of 9.18, and RER of 26.60. A visualization map was also generated to predict the level of quinoa in the adulterated samples. The results of this study demonstrate the ability of VNIR hyperspectral imaging for adulteration detection in quinoa flour as an alternative to the complicated traditional method.

Consideration on the Cleaning Effect of Black Contaminants using Visible-Near-Infrared (VNIR) Hyperspectral Images

VNIR hyperspectral images can be analyzed in situations involving color changes, such as before and after cleaning, without pre-treatment. In this study, to analyze the application of the VNIR hyperspectral system, we compared the black contaminants on the surface of the Jigwangguksa Pagoda upper stereobate at Beopcheonsa Temple, Wonju before and after cleaning with laser. After a preliminary experiment to evaluate the applicability of VNIR hyperspectral imaging, we took images of the four sides (north, south, east, and west). The 823 nm band image was used for comparing before and after cleaning. As per the result of the image differencing analysis about before and after cleaning, the spectral reflectance of the area where the cleaning was performed showed a tendency to increase. Additionally, it was found that not only the washed area but also the extent of change could be calculated through monochrome for quantitative comparison, and imaging analysis using the VNIR hyperspectral system was sufficiently effective for comparison before and after cleaning.

Visible near-infrared hyperspectral imaging and supervised classification for the detection of small intestinal necrosis tissue in vivo.

Complete recognition of necrotic areas during small bowel tissue resection remains challenging due to the lack of optimal intraoperative aid identification techniques. This research utilizes hyperspectral imaging techniques to automatically distinguish normal and necrotic areas of small intestinal tissue. Sample data were obtained from the animal model of small intestinal tissue of eight Japanese large-eared white rabbits developed by experienced physicians. A spectral library of normal and necrotic regions of small intestinal tissue was created and processed using six different supervised classification algorithms. The results show that hyperspectral imaging combined with supervised classification algorithms can be a suitable technique to automatically distinguish between normal and necrotic areas of small intestinal tissue. This new technique could aid physicians in objectively identify normal and necrotic areas of small intestinal tissue.

Vis-NIR hyperspectral imaging coupled with independent component analysis for saffron authentication

In the present contribution, visible-near infrared hyperspectral imaging (Vis-NIR-HSI) combined with a novel chemometric approach based on mean-filed independent component analysis (MF-ICA) followed by multivariate classification techniques is proposed for saffron authentication and adulteration detection. First, MF-ICA was used to exploit pure spatial and spectral profiles of the components. Then, principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used to find patterns of authentic samples based on their distribution maps. Then, detection of five common plant-derived adulterants of saffron including safflower, saffron style, calendula, rubia and turmeric were investigated. For this purpose, partial least squares-discriminant analysis (PLS-DA) for supervised classification to find a boundary between authentic and adulterated saffron samples. Classification accuracies for all models for calibration and prediction sets were 100 %. Finally, a mixed dataset was prepared and analyzed using the proposed strategy which again 100 % of accuracies for calibration and prediction sets were obtained. At the end, data driven soft independent modelling of class analogy (dd-SIMCA) was used to evaluate model for class modeling. Sensitivity was 95% for authentic class and specificities for all adulterants were 100%.

Assessing the Spectral Characteristics of Dye- and Pigment-Based Inkjet Prints by VNIR Hyperspectral Imaging.

Fine art photography, paper documents, and other parts of printing that aim to keep value are searching for credible techniques and mediums suitable for long-term archiving purposes. In general, long-lasting pigment-based inks are used for archival print creation. However, they are very often replaced or forged by dye-based inks, with lower fade resistance and, therefore, lower archiving potential. Frequently, the difference between the dye- and pigment-based prints is hard to uncover. Finding a simple tool for countrified identification is, therefore, necessary. This paper assesses the spectral characteristics of dye- and pigment-based ink prints using visible near-infrared (VNIR) hyperspectral imaging. The main aim is to show the spectral differences between these ink prints using a hyperspectral camera and subsequent hyperspectral image processing. Two diverse printers were exploited for comparison, a hobby dye-based EPSON L1800 and a professional pigment-based EPSON SC-P9500. The identical prints created via these printers on three different types of photo paper were recaptured by the hyperspectral camera. The acquired pixel values were studied in terms of spectral characteristics and principal component analysis (PCA). In addition, the obtained spectral differences were quantified by the selected spectral metrics. The possible usage for print forgery detection via VNIR hyperspectral imaging is discussed in the results.

Quantitative Evaluation of Food-Waste Components in Organic Fertilizer Using Visible–Near-Infrared Hyperspectral Imaging

Excessive addition of food waste fertilizer to organic fertilizer (OF) is forbidden in the Republic of Korea because of high sodium chloride and capsaicin concentrations in Korean food. Thus, rapid and nondestructive evaluation techniques are required. The objective of this study is to quantitatively evaluate food-waste components (FWCs) using hyperspectral imaging (HSI) in the visible–near-infrared (Vis/NIR) region. A HSI system for evaluating fertilizer components and prediction algorithms based on partial least squares (PLS) analysis and least squares support vector machines (LS-SVM) are developed. PLS and LS-SVM preprocessing methods are employed and compared to select the optimal of two chemometrics methods. Finally, distribution maps visualized using the LS-SVM model are created to interpret the dynamic changes in the OF FWCs with increasing FWC concentration. The developed model quantitively evaluates the OF FWCs with a coefficient of determination of 0.83 between the predicted and actual values. The developed Vis/NIR HIS system and optimized model exhibit high potential for OF FWC discrimination and quantitative evaluation.

Classification of Lingwu long jujube internal bruise over time based on visible near-infrared hyperspectral imaging combined with partial least squares-discriminant analysis

Early detection of internal bruise is one of the major challenges in postharvest quality sorting processes in Lingwu long jujube. In this study, the visible/near infrared (VIS/NIR) hyperspectral imaging system (400–1000 nm) was used to rapidly detect the intact and damaged jujube at five time points after mechanical damage (2 h, 4 h, 8 h, 12 h and 24 h). The region of interest of samples was selected by ENVI software, and the average spectrum was calculated for modelling. Different preprocessing methods were used to transform and enhance the spectral signal. Partial least squares-discriminant analysis (PLS-DA) classification models of the original and preprocessed spectra were established. The de-trending-PLS-DA model had the best effect, the accuracy of the calibration set and prediction set were 85.56% and 92.22%, respectively. The spectra were pre-processed by the de-trending algorithm, and the successive projection algorithm (SPA), uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS), interval variable iterative space shrinkage approach (iVISSA), cluster analysis (CA), two-dimensional correlation spectra (2D-COS), UVE-SPA, CARS-SPA, iVISSA-SPA, CA-SPA and 2D-COS-SPA method were used to select characteristic variables. The PLS-DA model based on feature variables was established. The de-trending-CARS-PLS-DA model with 63 variables was found to be the optimal model. In the de-trending-CARS-PLS-DA model, the accuracy of calibration set and prediction set were 86.67% and 91.11%, respectively. It was found that the model accurately detected bruising in jujube at 8 h after bruising, and the accuracy of calibration set and prediction set were 100%. The results showed that VIS/NIR hyperspectral imaging technology combined with PLS-DA could discriminate different stages of bruising in Lingwu long jujube. This study may help develop an online detection system of bruising in Lingwu long jujube.

Simultaneous quantification of chemical constituents in matcha with visible-near infrared hyperspectral imaging technology

This study aimed to assess the feasibility of identifying multiple chemical constituents in matcha using visible-near infrared hyperspectral imaging (VNIR-HSI) technology. Regions of interest (ROIs) were first defined in order to calculate the representative mean spectrum of each sample. Subsequently, the standard normal variate (SNV) method was applied to correct the characteristic spectra. Competitive adaptive reweighted sampling (CARS) and bootstrapping soft shrinkage (BOSS) were used to optimize the models. They were built based on partial least squares (PLS), creating two models referred to as CARS-PLS and BOSS-PLS. The BOSS-PLS models achieved best predictive accuracy, with coefficients of determination predicted to be 0.8077 for caffeine, 0.7098 for tea polyphenols (TPs), 0.7942 for free amino acids (FAAs), 0.8314 for the ratio of TPs to FAAs, and 0.8473 for chlorophyll. These findings highlight the potential of VNIR-HSI technology as a rapid and nondestructive alternative for simultaneous quantification of chemical constituents in matcha.