Comparison of Different CCD Detectors and Chemometrics for Predicting Total Anthocyanin Content and Antioxidant Activity of Mulberry Fruit Using Visible and Near Infrared Hyperspectral Imaging Technique

Fermentation is the most crucial step in black tea processing. In this study, we used a combination of Fourier transform near-infrared spectroscopy (FT-NIR) and computer vision system (CVS) for comprehensively evaluating the black tea fermentation degree. 110 samples of black tea at different fermentation degrees were collected in this research. The tea polyphenols (TP) content was measured using a UV–Vis spectrometer, and TP degradation during black tea fermentation was measured for classifying the degree of fermentation. Two mid-level strategies were used to analyze the fusion signals of FT-NIR and CVS. K-nearest neighbors, linear discriminant analysis, and a support vector machine (SVM) were applied for classification modeling. The advantages of FT-NIR and CVS were integrated based on mid-level fusion, and the results obtained were better than those obtained using independent methods for evaluating black tea fermentation. The mid-level fusion strategy that combined FT-NIR and CVS for principal component analysis feature extraction was the most effective in the SVM model. Classification accuracies of calibration and prediction sets were each 100%. Therefore, in this study, it was demonstrated that a combination of FT-NIR and CVS at mid-level fusion strategy can be used as a method to rapidly evaluate the black tea fermentation degree.

Intelligent evaluation of black tea fermentation degree by FT-NIR and computer vision based on data fusion strategy

Soil nutrient detection is important for precise fertilization. A total of 150 soil samples were picked from Lishui City. In this work, the total nitrogen (TN) content in soil samples was detected in the spectral range of 900–1700 nm using a hyperspectral imaging (HSI) system. Characteristic wavelengths were extracted using uninformative variable elimination (UVE) and the successive projections algorithm (SPA), separately. Partial least squares (PLS) and extreme learning machine (ELM) were used to establish the calibration models with full spectra and characteristic wavelengths, respectively. The results indicated that the prediction effect of the nonlinear ELM model was superior to the linear PLS model. In addition, the models using the characteristic wavelengths could also achieve good results, and the UVE–ELM model performed better, having a correlation coefficient of prediction (rp), root-mean-square error of prediction (RMSEP), and residual prediction deviation (RPD) of 0.9408, 0.0075, and 2.97, respectively. The UVE–ELM model was then used to estimate the TN content in the soil sample and obtain a distribution map. The research results indicate that HSI can be used for the detection and visualization of the distribution of TN content in soil, providing a basis for future large-scale monitoring of soil nutrient distribution and rational fertilization.

https://www.mdpi.com/1424-8220/19/20/4355/pdf

Quantitative Analysis of Soil Total Nitrogen Using Hyperspectral Imaging Technology with Extreme Learning Machine

Nondestructive measurement of pectin polysaccharides using hyperspectral imaging in mulberry fruit.

A data fusion approach on confocal Raman microspectroscopy and electronic nose for quantitative evaluation of pesticide residue in tea

An effective and rapid way to detect thiophanate-methyl residue on mulberry fruit is important for providing consumers with quality and safe of mulberry fruit. Chemical methods are complex, time-consuming, and costly, and can result in sample contamination. Rapid detection of thiophanate-methyl residue on mulberry fruit was studied using laser-induced breakdown spectroscopy (LIBS) and hyperspectral imaging (HSI) techniques. Principal component analysis (PCA) and partial least square regression (PLSR) were used to qualitatively and quantitatively analyze the data obtained by using LIBS and HSI on mulberry fruit samples with different thiophanate-methyl residues. The competitive adaptive reweighted sampling algorithm was used to select optimal variables. The results of model calibration were compared. The best result was given by the PLSR model that used the optimal preprocessed LIBS-HSI variables, with a correlation coefficient of 0.921 for the prediction set. The results of this research confirmed the feasibility of using LIBS and HSI for the rapid detection of thiophanate-methyl residue on mulberry fruit.

Feasibility of Laser-Induced Breakdown Spectroscopy and Hyperspectral Imaging for Rapid Detection of Thiophanate-Methyl Residue on Mulberry Fruit.

Mulberry trees are an important crop for sericulture. Pests can affect the yield and quality of mulberry leaves. This study aims to develop a hyperspectral imaging system in visible and near-infrared (NIR) region (400⁻1700 nm) for the rapid identification of Diaphania pyloalis larvae and its damage. The extracted spectra of five region of interests (ROI), namely leaf vein, healthy mesophyll, slight damage, serious damage, and Diaphania pyloalis larva at 400⁻1000 nm (visible range) and 900⁻1700 nm (NIR range), were used to establish a partial least squares discriminant analysis (PLS-DA) and least-squares support vector machines (LS-SVM) models. Successive projections algorithm (SPA), uninformation variable elimination (UVE), UVE-SPA, and competitive adaptive reweighted sampling were used for variable selection. The best models in distinguishing between leaf vein, healthy mesophyll, slight damage and serious damage, leaf vein, healthy mesophyll, and larva, slight damage, serious damage, and larva were all the SPA-LS-SVM models, based on the NIR range data, and their correct rate of prediction (CRP) were all 100.00%. The best model for the identification of all five ROIs was the UVE-SPA-LS-SVM model, based on visible range data, which had the CRP value of 97.30%. In summary, visible and near infrared hyperspectral imaging could distinguish Diaphania pyloalis larvae and their damage from leaf vein and healthy mesophyll in a rapid and non-destructive way.

Xiaoqiang Du

Papers

Nondestructive measurement of pectin polysaccharides using hyperspectral imaging in mulberry fruit.

Feasibility of Laser-Induced Breakdown Spectroscopy and Hyperspectral Imaging for Rapid Detection of Thiophanate-Methyl Residue on Mulberry Fruit.

Potential of Visible and Near-Infrared Hyperspectral Imaging for Detection of Diaphania pyloalis Larvae and Damage on Mulberry Leaves.