Preface on “Advanced Infrared Technology and Applications – AITA 2021”

Rapid and real-time prediction of lactic acid bacteria (LAB) in farmed salmon flesh using near-infrared (NIR) hyperspectral imaging combined with chemometric analysis

Soil texture is one of the important factors affecting the physical and chemical properties of soil, and is generally divided into sand, silt, and clay. Understanding its spatial distribution is crucial for agricultural and environmental decision-making. Estimation of soil texture fractions (STFs) can be achieved through visible, near-infrared, and shortwave infrared (VNIR-SWIR) spectra, making hyperspectral imaging technology particularly promising for low-cost and large-scale monitoring of STFs. However, the process of image acquisition is inevitably affected by soil environmental factors. In this study, a soil texture fractions estimation method for hyperspectral images was proposed based on Orthogonal Signal Correction (OSC). Firstly, the OSC algorithm was applied to eliminate the impact of soil environmental factors on the image spectra. Then, the spectral feature was enhanced by Fractional Order Derivative (FOD). Subsequently, a Partial Least Squares Regression (PLSR) model was established based on the optimal bands selected by Competitive Adaptive Reweighted Sampling (CARS). A total of 107 soil samples were obtained from the agricultural area in Lishu County, Jilin Province, China. The hyperspectral images of the ZY1-02D and ZY1-02E satellites were acquired simultaneously in this area. The results showed that the OSC algorithm effectively reduced the influence of soil environmental factors on image spectra, and significantly improved the accuracy of STFs estimation. After applying OSC to the original image spectra, the estimation accuracies (R2) for sand, silt, and clay content were improved from 0.52, 0.51, 0.50 to 0.81, 0.65, and 0.70 respectively. Compared to External Parameter Orthogonalization (EPO) correction, the model accuracy and stability were higher with OSC correction. The selected results from CARS indicated that the characteristic spectral bands of soil texture are concentrated around 500 nm, 750 nm, 900 nm, and 2200 nm. Further analysis indicates that the distribution trends of the soil texture fractions in this study were consistent with previous studies, demonstrating the good applicability of the estimation method.

Wheat protein and fat contents directly affect the flavor and yield of liquor. This study utilized hyperspectral imaging (HSI) technology along with an integrated learning model to rapidly and non-destructively detect protein and fat contents in wheat. The hyperspectral data of the wheat grains was preprocessed using various methods. The competitive adaptive reweighted sampling (CARS) algorithm, the successive projections algorithm (SPA), and the combination of the two algorithms (CARS-SPA) were used to extract the characteristic wavelengths. Three support vector regression (SVR) models were created to forecast wheat protein and fat contents using full-band spectral data and characteristic wavelengths. These models are gray wolf optimized support vector regression (GWO-SVR), particle swarm optimized support vector regression (PSO-SVR), and support vector regression-based integrated learning (AdaBoost-SVR). Comparing the models showed that the AdaBoost-SVR model, utilizing the Eigen wavelengths extracted through the CARS-SPA and CARS algorithms, stood out as the most accurate in predicting wheat protein and fat contents, with Rp 2 values of 0.9789 and 0.9651, respectively. The study findings indicate that combining HSI with AdaBoost-SVR integrated learning modeling enables rapid, non-destructive determination of wheat protein and fat contents with greater accuracy than using a single machine learning (ML) model.

Hyperspectral imaging technology was used to assess the soluble solids content (SSC), titrable acidity content (TAC), and firmness of hami melons. The mean spectra were extracted from the regions of interest (ROI) of the hyperspectral images of each hami melon. Spectral data were first pretreated with different preprocessing methods and analyzed using the partial least squares (PLS) method to build calibration models. However, full spectral data contain a great number of redundant and colinear variables that lead to poor predicting ability of calibration models. Three typical variable selection methods, i.e., the genetic algorithm (GA), successive projections algorithm (SPA), and competitive adaptive reweighted sampling (CARS) were used to extract effective wavelength variables from the full spectra for determination of SSC, TAC, and firmness of hami melon. The results showed that the SNV‐CARS‐PLS model based on 51 and 40 wavelength variables achieved the optimal performance for SSC and TAC compared with full spectral SNV‐PLS, SNV‐GA‐PLS, and SNV‐SPA‐PLS models. The RP, RMSEP, and RPD of SNV‐CARS‐PLS model for SSC were 0.9606, 0.3816, and 3.598, respectively, and 0.9125, 0.0263, and 2.445 for TAC, respectively. The best model for predicting firmness was RAW‐CARS‐PLS model based on 57 wavelength variables with the RP RMSEP, and RDP values of 0.8671, 20.05, and 1.996, respectively. Overall, the results indicate that the CARS is a powerful method for selecting effective wavelength variables and demonstrated the feasibility of using hyperspectral imaging technology as a fast and nondestructive method for simultaneous detection of SSC, TAC, and firmness of hami melons.Practical applicationsThe quality of melon has a direct relationship with the soluble solids content, titrable acidity content, and texture of melon. To ensure the best taste and highest nutritional value, it is better for melons to be harvested when completely mature. Nowadays, melons were harvested according to different fruit development times after flowering. Traditional methods for detection of internal qualities and maturity of melon mainly focus on the appearance of melon and depend on the feelings of professionals, which are tedious, time‐consuming, expensive, and greatly influenced by subjective factors. More than anything, Traditional methods are hard to be highly accurate. Hyperspectral imaging technology has the advantages of rapid and nondestructive. The results showed that hyperspectral imaging technology for the simultaneous detection of soluble solids content, titrable acidity content, and firmness of melon is feasible. And the results can provide a reference for detecting large fruits by using hyperspectral imaging technology

Prediction of TVB-N content in beef with packaging films using visible-near infrared hyperspectral imaging

<b><sc>Abstract.</sc> </b>As a mycotoxin produced by the causal pathogen Fusarium graminearum, Deoxynivalenol (DON) poses serious health risks to both humans and livestock. Due to these health concerns, barley used for malting, food or feed is routinely assayed for DON levels. In this study, the feasibility of using hyperspectral imaging (382–1030 nm) to develop a rapid and non-destructive protocol for assaying DON in barley kernels was explored. It was found that partial least square discriminant analysis (PLSDA) was able to discriminate kernels into separate classes corresponding to their DON levels. Competitive adaptive reweighted sampling (CARS) was used to choose potential feature wavelengths, and these selected variables were further optimized using the iterative selection of successive projections algorithm (ISSPA). CARS-ISSPA-PLSDA discriminated barley kernels having lower DON (less than1.25 mg/kg) levels from those with higher levels with high accuracy. The results demonstrate that hyperspectral imaging have potential for accelerating non-destructive DON assays of barley samples.

Non-destructive assessment of kiwifruit quality is critical for postharvest preservation and grading. This paper proposes a novel quantitative evaluation method for the kiwifruit comprehensive quality index (KCQI) during shelf life, based on hyperspectral imaging (HSI) combined with a one-dimensional convolutional neural network (1D-CNN). Hyperspectral images of two kiwifruit cultivars were acquired at four shelf-life stages using an HSI system, and six quality parameters were measured as reference standards. Based on correlation and factor analyses, five key parameters—soluble solids content, firmness, L*, b*, and chroma—were selected to construct the KCQI. Three spectral band selection methods and three modeling algorithms were compared, with the competitive adaptive reweighted sampling (CARS)–1D-CNN model yielding the highest prediction accuracy ( = 0.82, RMSEP = 0.26, RPDP = 2.39). Subsequently, a spatial distribution map was generated to visualize the KCQI. These results demonstrate the potential of the HSI–1D-CNN approach for accurate postharvest quality monitoring and intelligent grading.

Near‐infrared (NIR) hyperspectral imaging combined with multivariate analyses were used to execute adulterants detection and authenticity discrimination for coarse grain flours including millet, corn, and soybean. Spectral data in the range of 865–1,711 nm were first extracted and preprocessed from a total of 1,080 samples of six categories of pure and adulterated flours. Principal component analysis was employed to generate scores scatter plots for differentiating specific grouping of samples. Successive projection algorithm (SPA) and competitive adaptive reweighted sampling (CARS) were adopted to select the effective wavelengths. Then identification models were developed by partial least squares discriminant analysis (PLS‐DA) using effective wavelengths and full spectra (FS) as inputs, respectively. The results indicated all of models provided the satisfactory identification rate, over 94.8% for adulterated flours, and even achieved 100% for pure flours. Considering computational cost reduction and model simplification, PLS‐DA‐CARS and PLS‐DA‐SPA models were more effective in practice within an acceptable range in comparison with PLS‐DA‐FS model. Especially, PLS‐DA‐SPA model could achieve higher identification accuracy than PLS‐DA‐CARS model. It can be concluded that NIR hyperspectral imaging technology as a noncontact and quick analytical method has promising application prospects for quality control of flour products.Practical ApplicationsCoarse grain flours and its products are more popular with people in recent years because they contain abundant vitamins, dietary fiber, microelements, and other nutrients. Driven by huge profits, some coarse grain flours such as millet, corn, and soybean are often sold in term of adulterated flours, which not only disrupts market order, but also results in quality degradation and great economic losses. As a noncontact and quick analytical technology, NIR hyperspectral imaging was used for detecting the adulteration of coarse grain flours to eliminate fraudulent or unfair practices. Multivariate analysis methods were employed to speed the detection process and improve operation efficiency. The results indicated that NIR hyperspectral imaging technology was feasible and effective in discriminating the authenticity of coarse grain flours, laying a foundation for designing a portable inspection device with the optical filters.

Hyperspectral imaging coupled with multivariate methods for seed vitality estimation and forecast for Quercus variabilis.

Prediction of moisture content of wood using Modified Random Frog and Vis-NIR hyperspectral imaging

Hyperspectral imaging was explored to detect Sclerotinia stem rot (SSR) on oilseed rape leaves with chemometric methods, and the influences of variable selection, machine learning, and calibration transfer methods on detection performances were evaluated. Three different sample sets containing healthy and infected oilseed rape leaves were acquired under different imaging acquisition parameters. Four discriminant models were built using full spectra, including partial least squares-discriminant analysis (PLS-DA), support vector machine (SVM), soft independent modeling of class analogies (SIMCA), and k-nearest neighbors (KNN). PLS-DA and SVM models were also built with the optimal wavelengths selected by principal component analysis (PCA) loadings, second derivative spectra, competitive adaptive reweighted sampling (CARS), and successive projections algorithm (SPA). The optimal wavelengths selected for each sample set by different methods were different; however, the optimal wavelengths selected by PCA loadings and second derivative spectra showed similarity between different sample sets. Direct standardization (DS) was successfully applied to reduce spectral differences among different sample sets. Overall, the results demonstrated that using hyperspectral imaging with chemometrics for plant disease detection can be efficient and will also help in the selection of optimal variable selection, machine learning, and calibration transfer methods for fast and accurate plant disease detection.

Rapid and nondestructive identification of rice storage year using hyperspectral technology

Accurate determination of Soil Organic Carbon (SOC), which is the foundation of soil health and safeguards ecological and food security, is crucial in local agricultural production. We aimed to investigate the influence of soil texture on hyperspectral models for predicting SOC content and to evaluate the role of different preprocessing methods and feature band selection algorithms in improving modeling efficiency. Laboratory-determined SOC content and hyperspectral reflectance data were obtained using soil samples from daylily cultivation areas in Yunzhou District, Datong City. Mathematical transformations, including Savitzky–Golay smoothing (SG), First Derivative (FD), Second Derivative (SD), Multiplicative Scatter Correction (MSC), and Standard Normal Variate (SNV), were applied to the spectral reflectance data. Feature bands extracted based on the successive projection algorithm (SPA) and Competitive Adaptive Reweighted Sampling (CARS) were used to establish SOC content inversion models employing four algorithms: partial least-squares regression (PLSR), Random Forest (RF), Backpropagation Neural Network (BP), and Convolutional Neural Network (CNN). The results indicate the following: (1) Preprocessing can effectively increase the correlation between the soil spectral reflectance process and SOC content. (2) SPA and CARS effectively screened the characteristic bands of SOC in daylily cultivated soil from the spectral curves. The SPA algorithm and CARS selected 4–11 and 9–122 bands, respectively, and both algorithms facilitated model construction. (3) Among all the constructed models, the FD-CARS-PLSR performed most prominently, with coefficients of determination (R2) for the training and validation sets reaching 0.93 and 0.83, respectively, demonstrating high model stability and reliability. (4) Incorporating soil texture as an auxiliary variable into the PLSR inversion model improved the inversion accuracy, with accuracy gains ranging between 0.01 and 0.05.

Hyperspectral transmission images of brown shell eggs were collected by using hyperspectral imager, and the Huff value and yolk index of eggs were measured by electronic balance and vernier caliper respectively. Principal component analysis was used to calculate the weight of egg huff value and yolk index, both of which were 0.5. The two indexes were used to construct the egg freshness fusion index. After comparing the modeling effects of different spectral pretreatments, the full spectrum after the first-order differential pretreatment was selected for subsequent data processing. The characteristic wavelengths which are closely related to the fusion index of egg freshness are selected by using the uninformative variables elimination (UVE), stepwise regression algorithm (STP) and successive projections algorithm (SPA) respectively, and the prediction performance of the model based on the characteristic wavelength is achieved. The results showed that there were 12, 5 and 10 characteristic wavelengths which were closely related to the fusion index of egg freshness selected by UVE, STP and SPA. Among them, the partial least squares (PLS) model established based on 10 characteristic wavelengths screened out by SPA has similar effect to the and multiple linear regression (MLR) model. The correlation coefficients of the PLS model in the training set and the prediction set were 0.860 and 0.856 respectively. The root-mean-square errors were 3.487 and 4.764 respectively, and the RPD of the prediction set was 1.937, which is the optimal model to predict the egg freshness fusion index. The modeling variables were reduced from 438 to 10, the structure of the model was greatly simplified, and the model could predict the egg freshness fusion index non-destructively.

BackgroundGray mold and anthracnose are the main factors affecting strawberry quality and yield. Accurate and rapid early disease identification is of great significance to achieve precise targeted spraying to avoid large-scale spread of diseases and improve strawberry yield and quality. However, the characteristics between early disease infected and healthy leaves are very similar, making the early identification of strawberry gray mold and anthracnose still a challenge.ResultsBased on hyperspectral imaging technology, this study explored the potential of combining spectral fingerprint features and vegetation indices (VIs) for early detection (24-h infected) of strawberry leaves diseases. The competitive adaptive reweighted sampling (CARS) algorithm and ReliefF algorithm were used for the extraction of spectral fingerprint features and VIs, respectively. Three machine learning models, Backpropagation Neural Network (BPNN), Support Vector Machine (SVM) and Random Forest (RF), were developed for the early identification of strawberry gray mold and anthracnose, using spectral fingerprint, VIs and their combined features as inputs respectively. The results showed that the combination of spectral fingerprint features and VIs had better recognition accuracy compared with individual features as inputs, and the accuracies of the three classifiers (BPNN, SVM and RF) were 97.78%, 94.44%, and 93.33%, respectively, which indicate that the fusion features approach proposed in this study can effectively improve the early detection performance of strawberry leaves diseases.ConclusionsThis study provided an accurate, rapid, and nondestructive recognition of strawberry gray mold and anthracnose disease in early stage.