Combining Fourier Transform Mid-Infrared Spectroscopy with Chemometric Methods to Detect Adulterations in Milk Powder.

Adulteration is one of the major concerns among all the quality problems of milk powder. Soybean flour and rice flour are harmless adulterations in the milk powder. In this study, mid-infrared spectroscopy was used to detect the milk powder adulterated with rice flour or soybean flour and simultaneously determine the adulterations content. Partial least squares (PLS), support vector machine (SVM) and extreme learning machine (ELM) were used to establish classification and regression models using full spectra and optimal wavenumbers. ELM models using the optimal wavenumbers selected by principal component analysis (PCA) loadings obtained good results with all the sensitivity and specificity over 90%. Regression models using the full spectra and the optimal wavenumbers selected by successive projections algorithm (SPA) obtained good results, with coefficient of determination (R2) of calibration and prediction all over 0.9 and the predictive residual deviation (RPD) over 3. The classification results of ELM models and the determination results of adulterations content indicated that the mid-infrared spectroscopy was an effective technique to detect the rice flour and soybean flour adulteration in the milk powder. This study would help to apply mid-infrared spectroscopy to the detection of adulterations such as rice flour and soybean flour in real-world conditions.

Identification of Soybean Varieties Using Hyperspectral Imaging Coupled with Convolutional Neural Network.

Soybean variety is connected to stress resistance ability, as well as nutritional and commercial value. Near-infrared hyperspectral imaging was applied to classify three varieties of soybeans (Zhonghuang37, Zhonghuang41, and Zhonghuang55). Pixel-wise spectra were extracted and preprocessed, and average spectra were also obtained. Convolutional neural networks (CNN) using the average spectra and pixel-wise spectra of different numbers of soybeans were built. Pixel-wise CNN models obtained good performance predicting pixel-wise spectra and average spectra. With the increase of soybean numbers, performances were improved, with the classification accuracy of each variety over 90%. Traditionally, the number of samples used for modeling is large. It is time-consuming and requires labor to obtain hyperspectral data from large batches of samples. To explore the possibility of achieving decent identification results with few samples, a majority vote was also applied to the pixel-wise CNN models to identify a single soybean variety. Prediction maps were obtained to present the classification results intuitively. Models using pixel-wise spectra of 60 soybeans showed equivalent performance to those using the average spectra of 810 soybeans, illustrating the possibility of discriminating soybean varieties using few samples by acquiring pixel-wise spectra.

Near-Infrared Hyperspectral Imaging Combined with Deep Learning to Identify Cotton Seed Varieties.

Cotton seed purity is a critical factor influencing the cotton yield. In this study, near-infrared hyperspectral imaging was used to identify seven varieties of cotton seeds. Score images formed by pixel-wise principal component analysis (PCA) showed that there were differences among different varieties of cotton seeds. Effective wavelengths were selected according to PCA loadings. A self-design convolution neural network (CNN) and a Residual Network (ResNet) were used to establish classification models. Partial least squares discriminant analysis (PLS-DA), logistic regression (LR) and support vector machine (SVM) were used as direct classifiers based on full spectra and effective wavelengths for comparison. Furthermore, PLS-DA, LR and SVM models were used for cotton seeds classification based on deep features extracted by self-design CNN and ResNet models. LR and PLS-DA models using deep features as input performed slightly better than those using full spectra and effective wavelengths directly. Self-design CNN based models performed slightly better than ResNet based models. Classification models using full spectra performed better than those using effective wavelengths, with classification accuracy of calibration, validation and prediction sets all over 80% for most models. The overall results illustrated that near-infrared hyperspectral imaging with deep learning was feasible to identify cotton seed varieties.

Detection of Oil Chestnuts Infected by Blue Mold Using Near-Infrared Hyperspectral Imaging Combined with Artificial Neural Networks.

Mildew damage is a major reason for chestnut poor quality and yield loss. In this study, a near-infrared hyperspectral imaging system in the 874⁻1734 nm spectral range was applied to detect the mildew damage to chestnuts caused by blue mold. Principal component analysis (PCA) scored images were firstly employed to qualitatively and intuitively distinguish moldy chestnuts from healthy chestnuts. Spectral data were extracted from the hyperspectral images. A successive projections algorithm (SPA) was used to select 12 optimal wavelengths. Artificial neural networks, including back propagation neural network (BPNN), evolutionary neural network (ENN), extreme learning machine (ELM), general regression neural network (GRNN) and radial basis neural network (RBNN) were used to build models using the full spectra and optimal wavelengths to distinguish moldy chestnuts. BPNN and ENN models using full spectra and optimal wavelengths obtained satisfactory performances, with classification accuracies all surpassing 99%. The results indicate the potential for the rapid and non-destructive detection of moldy chestnuts by hyperspectral imaging, which would help to develop online detection system for healthy and blue mold infected chestnuts.

Identification of Maize Kernel Vigor under Different Accelerated Aging Times Using Hyperspectral Imaging.

Seed aging during storage is irreversible, and a rapid, accurate detection method for seed vigor detection during seed aging is of great importance for seed companies and farmers. In this study, an artificial accelerated aging treatment was used to simulate the maize kernel aging process, and hyperspectral imaging at the spectral range of 874⁻1734 nm was applied as a rapid and accurate technique to identify seed vigor under different accelerated aging time regimes. Hyperspectral images of two varieties of maize processed with eight different aging duration times (0, 12, 24, 36, 48, 72, 96 and 120 h) were acquired. Principal component analysis (PCA) was used to conduct a qualitative analysis on maize kernels under different accelerated aging time conditions. Second-order derivatization was applied to select characteristic wavelengths. Classification models (support vector machine-SVM) based on full spectra and optimal wavelengths were built. The results showed that misclassification in unprocessed maize kernels was rare, while some misclassification occurred in maize kernels after the short aging times of 12 and 24 h. On the whole, classification accuracies of maize kernels after relatively short aging times (0, 12 and 24 h) were higher, ranging from 61% to 100%. Maize kernels with longer aging time (36, 48, 72, 96, 120 h) had lower classification accuracies. According to the results of confusion matrixes of SVM models, the eight categories of each maize variety could be divided into three groups: Group 1 (0 h), Group 2 (12 and 24 h) and Group 3 (36, 48, 72, 96, 120 h). Maize kernels from different categories within one group were more likely to be misclassified with each other, and maize kernels within different groups had fewer misclassified samples. Germination test was conducted to verify the classification models, the results showed that the significant differences of maize kernel vigor revealed by standard germination tests generally matched with the classification accuracies of the SVM models. Hyperspectral imaging analysis for two varieties of maize kernels showed similar results, indicating the possibility of using hyperspectral imaging technique combined with chemometric methods to evaluate seed vigor and seed aging degree.

Variety Identification of Raisins Using Near-Infrared Hyperspectral Imaging.

Different varieties of raisins have different nutritional properties and vary in commercial value. An identification method of raisin varieties using hyperspectral imaging was explored. Hyperspectral images of two different varieties of raisins (Wuhebai and Xiangfei) at spectral range of 874⁻1734 nm were acquired, and each variety contained three grades. Pixel-wise spectra were extracted and preprocessed by wavelet transform and standard normal variate, and object-wise spectra (sample average spectra) were calculated. Principal component analysis (PCA) and independent component analysis (ICA) of object-wise spectra and pixel-wise spectra were conducted to select effective wavelengths. Pixel-wise PCA scores images indicated differences between two varieties and among different grades. SVM (Support Vector Machine), k-NN (k-nearest Neighbors Algorithm), and RBFNN (Radial Basis Function Neural Network) models were built to discriminate two varieties of raisins. Results indicated that both SVM and RBFNN models based on object-wise spectra using optimal wavelengths selected by PCA could be used for raisin variety identification. The visualization maps verified the effectiveness of using hyperspectral imaging to identify raisin varieties.

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.

Grading of Chinese Cantonese Sausage Using Hyperspectral Imaging Combined with Chemometric Methods.

Fast and accurate grading of Chinese Cantonese sausage is an important concern for customers, organizations, and the industry. Hyperspectral imaging in the spectral range of 874-1734 nm, combined with chemometric methods, was applied to grade Chinese Cantonese sausage. Three grades of intact and sliced Cantonese sausages were studied, including the top, first, and second grades. Support vector machine (SVM) and random forests (RF) techniques were used to build two different models. Second derivative spectra and RF were applied to select optimal wavelengths. The optimal wavelengths were the same for intact and sliced sausages when selected from second derivative spectra, while the optimal wavelengths for intact and sliced sausages selected using RF were quite similar. The SVM and RF models, using full spectra and the optimal wavelengths, obtained acceptable results for intact and sliced sausages. Both models for intact sausages performed better than those for sliced sausages, with a classification accuracy of the calibration and prediction set of over 90%. The overall results indicated that hyperspectral imaging combined with chemometric methods could be used to grade Chinese Cantonese sausages, with intact sausages being better suited for grading. This study will help to develop fast and accurate online grading of Cantonese sausages, as well as other sausages.

Aureusidin ameliorates 6-OHDA-induced neurotoxicity via activating Nrf2/HO-1 signaling pathway and preventing mitochondria-dependent apoptosis pathway in SH-SY5Y cells and Caenorhabditis elegans.

Movement disorder Parkinson's disease (PD) is the second most common neurodegenerative disease in the world after Alzheimer's disease, which severely affects the quality of patients' lives and imposes an increasingly heavy socioeconomic burden. Aureusidin is a kind of natural flavonoid compound with anti-inflammatory and anti-oxidant activities, while its pharmacological action and mechanism are rarely reported in PD. This study aimed to explore the neuroprotective effects and potential mechanisms of Aureusidin in PD. The present study demonstrated that Aureusidin protected SH-SY5Y cells from cell damage induced by 6-hydroxydopamine (6-OHDA) via inhibiting the mitochondria-dependent apoptosis and activating the Nrf2/HO-1 antioxidant signaling pathway. Additionally, Aureusidin diminished dopaminergic (DA) neuron degeneration induced by 6-OHDA and reduced the aggregation toxicity of α-synuclein (α-Syn) in Caenorhabditis elegans (C. elegans.) In conclusion, Aureusidin showed a neuroprotective effect in the 6-OHDA-induced PD model via activating Nrf2/HO-1 signaling pathway and prevented mitochondria-dependent apoptosis pathway, and these findings suggested that Aureusidin may be an effective drug for the treatment of PD.

Pongamol Prevents Neurotoxicity via the Activation of MAPKs/Nrf2 Signaling Pathway in H2O2-Induced Neuronal PC12 Cells and Prolongs the Lifespan of Caenorhabditis elegans.

Despite tremendous advances in modern medicine, effective prevention or therapeutic strategies for age-related neurodegenerative diseases such as Alzheimer's disease (AD) remain limited. Growing evidence now suggests that oxidative stress and apoptosis are increasingly associated with AD as promising therapeutic targets. Pongamol, a flavonoid, is the main constituent of pongamia pinnata and possesses a variety of pharmacological activities such as antioxidant, anti-aging and anti-inflammatory. In the present study, we investigated the antioxidant effects and mechanisms of pongamol in H2O2-induced PC12 cells and Caenorhabditis elegans (C. elegans). Our findings revealed that pongamol reduced cellular damage and apoptosis in H2O2-induced PC12 cells. Furthermore, pongamol reduced levels of apoptosis-related proteins Bax, Cyto C, Cleaved Caspase-3, and Cleaved PARP1, and increased the level of anti-apoptotic protein Bcl-2. Pongamol also effectively attenuated the level of oxidative stress markers such as glutathione (GSH) and reactive oxygen species (ROS) in H2O2-induced PC12 cells. Additionally, pongamol possessed antioxidant activity in H2O2-induced PC12 cells through the MAPKs/Nrf2 signaling pathway. Furthermore, pongamol exerted neuroprotective and anti-aging effects in C. elegans. All together, these results suggested that pongamol has a potential neuroprotective effect through the modulation of MAPKs/Nrf2 signaling pathway.

Pongamol Alleviates Neuroinflammation and Promotes Autophagy in Alzheimer's Disease by Regulating the Akt/mTOR Signaling Pathway.

Alzheimer's disease (AD), one of the neurodegenerative disorders, is highly correlated with the abnormal hyperphosphorylation of Tau and aggregation of ß-amyloid (Aß). Oxidative stress, neuroinflammation, and abnormal autophagy are key drivers of AD and how they contribute to neuropathology remains largely unknown. The flavonoid compound pongamol is reported to possess a variety of pharmacological activities, such as antioxidant, antibacterial, and anti-inflammatory. This study investigated the neuroprotective effect and its mechanisms of pongamol in lipopolysaccharide (LPS)-induced BV2 cells, d-galactose/sodium nitrite/aluminum chloride (d-gal/NaNO2/AlCl3)-induced AD mice, and Caenorhabditis elegans models. Our research revealed that pongamol reduced the release of inflammatory factors IL-1ß, TNF-α, COX-2, and iNOS in LPS-induced BV2 cells. Pongamol also protected neurons and significantly restored memory function, inhibited Tau phosphorylation, downregulated Aß aggregation, and increased oxidoreductase activity in the hippocampus of AD mice. In addition, pongamol reversed the nuclear transfer of NF-κB and increased the levels of Beclin 1 and LC3 II/LC3 I. Most importantly, the anti-inflammatory and promoter autophagy effects of pongamol may be related to the regulation of the Akt/mTOR signaling pathway. In summary, these results showed that pongamol has a potential neuroprotective effect, which greatly enriched the research on the pharmacological activity of pongamol for improving AD.

Application of Visible/Infrared Spectroscopy and Hyperspectral Imaging With Machine Learning Techniques for Identifying Food Varieties and Geographical Origins.

Food quality and safety are strongly related to human health. Food quality varies with variety and geographical origin, and food fraud is becoming a threat to domestic and global markets. Visible/infrared spectroscopy and hyperspectral imaging techniques, as rapid and non-destructive analytical methods, have been widely utilized to trace food varieties and geographical origins. In this review, we outline recent research progress on identifying food varieties and geographical origins using visible/infrared spectroscopy and hyperspectral imaging with the help of machine learning techniques. The applications of visible, near-infrared, and mid-infrared spectroscopy as well as hyperspectral imaging techniques on crop food, beverage, fruits, nuts, meat, oil, and some other kinds of food are reviewed. Furthermore, existing challenges and prospects are discussed. In general, the existing machine learning techniques contribute to satisfactory classification results. Follow-up researches of food varieties and geographical origins traceability and development of real-time detection equipment are still in demand.

Investigation on Data Fusion of Multisource Spectral Data for Rice Leaf Diseases Identification Using Machine Learning Methods.

Rice diseases are major threats to rice yield and quality. Rapid and accurate detection of rice diseases is of great importance for precise disease prevention and treatment. Various spectroscopic techniques have been used to detect plant diseases. To rapidly and accurately detect three different rice diseases [leaf blight (Xanthomonas oryzae pv. Oryzae), rice blast (Pyricularia oryzae), and rice sheath blight (Rhizoctonia solani)], three spectroscopic techniques were applied, including visible/near-infrared hyperspectral imaging (HSI) spectra, mid-infrared spectroscopy (MIR), and laser-induced breakdown spectroscopy (LIBS). Three different levels of data fusion (raw data fusion, feature fusion, and decision fusion) fusing three different types of spectral features were adopted to categorize the diseases of rice. Principal component analysis (PCA) and autoencoder (AE) were used to extract features. Identification models based on each technique and different fusion levels were built using support vector machine (SVM), logistic regression (LR), and convolution neural network (CNN) models. Models based on HSI performed better than those based on MIR and LIBS, with the accuracy over 93% for the test set based on PCA features of HSI spectra. The performance of rice disease identification varied with different levels of fusion. The results showed that feature fusion and decision fusion could enhance identification performance. The overall results illustrated that the three techniques could be used to identify rice diseases, and data fusion strategies have great potential to be used for rice disease detection.

Identifying Freshness of Spinach Leaves Stored at Different Temperatures Using Hyperspectral Imaging.

Spinach is prone to spoilage in the course of preservation. Spinach leaves stored at different temperatures for different durations will have varying degrees of freshness. In order to monitor the freshness of spinach leaves during storage, a rapid and non-destructive method-hyperspectral imaging technology-was applied in this study. Visible near-infrared reflectance (Vis-NIR) (380-1030 nm) and near-infrared reflectance (NIR) (874-1734 nm) hyperspectral imaging systems were used. Spinach leaves preserved at different temperatures with different durations (0, 3, 6, 9 days at 4 °C and 0, 1, 2 days at 20 °C) were studied. Principal component analysis (PCA) was adopted as a qualitative analysis method. The second-order derivative spectra were utilized to select effective wavelengths. Partial least squares discriminant analysis (PLS-DA), support vector machine (SVM), and extreme learning machine (ELM) were used to build models based on full spectra and effective wavelengths. All three models achieved good results, with accuracies above 92% for both Vis-NIR spectra and NIR spectra. ELM obtained the best results, with all accuracies reaching 100%. The overall results indicate the possibility of the freshness identification of spinach preserved at different temperatures for different durations using two kinds of hyperspectral imaging systems.

Variety identification of oat seeds using hyperspectral imaging: investigating the representation ability of deep convolutional neural network.

Variety identification of seeds is critical for assessing variety purity and ensuring crop yield. In this paper, a novel method based on hyperspectral imaging (HSI) and deep convolutional neural network (DCNN) was proposed to discriminate the varieties of oat seeds. The representation ability of DCNN was also investigated. The hyperspectral images with a spectral range of 874-1734 nm were primarily processed by principal component analysis (PCA) for exploratory visual distinguishing. Then a DCNN trained in an end-to-end manner was developed. The deep spectral features automatically learnt by DCNN were extracted and combined with traditional classifiers (logistic regression (LR), support vector machine with RBF kernel (RBF_SVM) and linear kernel (LINEAR_SVM)) to construct discriminant models. Contrast models were built based on the traditional classifiers using full wavelengths and optimal wavelengths selected by the second derivative (2nd derivative) method. The comparison results showed that all DCNN-based models outperformed the contrast models. DCNN trained in an end-to-end manner achieved the highest accuracy of 99.19% on the testing set, which was finally employed to visualize the variety classification. The results demonstrated that the deep spectral features with outstanding representation ability enabled HSI together with DCNN to be a reliable tool for rapid and accurate variety identification, which would help to develop an on-line system for quality detection of oat seeds as well as other grain seeds.

Hyperspectral imaging for seed quality and safety inspection: a review.

Hyperspectral imaging has attracted great attention as a non-destructive and fast method for seed quality and safety assessment in recent years. The capability of this technique for classification and grading, viability and vigor detection, damage (defect and fungus) detection, cleanness detection and seed composition determination is illustrated by presentation of applications in quality and safety determination of seed in this review. The summary of hyperspectral imaging technology for seed quality and safety inspection for each category is also presented, including the analyzed spectral range, sample varieties, sample status, sample numbers, features (spectral features, image features, feature extraction methods), signal mode and data analysis strategies. The successful application of hyperspectral imaging in seed quality and safety inspection proves that many routine seed inspection tasks can be facilitated with hyperspectral imaging.

Phospho­regulation of Cdc14A by polo­like kinase 1 is involved in ß­cell function and cell cycle regulation.

The objective of the present study was to investigate the effects of polo­like kinase 1 (PLK1) and the phosphorylation of human cell division cycle protein 14A (Cdc14A) by PLK1 on ß­cell function and cell cycle regulation. Mouse ß­TC3 cells were incubated with small interfering RNA (siRNA) to knock down the expression of PLK1. Cell cycle analysis was performed using flow cytometry, and cell proliferation and apoptosis was determined. Insulin secretion was evaluated by a radioimmunoassay under both low and high glucose conditions. Mouse ß­TC3 cells were transfected with a wild type or a non­phosphorylatable Cdc14A mutant (Cdc14AS351A/363A; Cdc14AAA) to investigate whether the phosphorylation of Cdc14A is involved in cellular regulation of PLK1 under high glucose conditions. It was found that PLK1 siRNA significantly promoted cellular apoptosis, inhibited cell proliferation, decreased insulin secretion and reduced Cdc14A expression under both low and high glucose conditions. Cdc14A overexpression promoted ß­TC3 cell proliferation and insulin secretion, while Cdc14AAA overexpression inhibited cell proliferation and insulin secretion under high glucose conditions. PLK1 siRNA partially reversed the proliferation­promoting effects of Cdc14A and further intensified the inhibition of proliferation by Cdc14AAA under high glucose conditions. Similarly, Cdc14A overexpression partially reversed the insulin­inhibiting effects of PLK1 siRNA, while Cdc14AAA overexpression showed a synergistic inhibitory effect on insulin secretion with PLK1 siRNA under high glucose conditions. In conclusion, PLK1 promoted cell proliferation and insulin secretion while inhibiting cellular apoptosis in ß­TC3 cell lines under both low and high glucose conditions. In addition, the phospho­regulation of Cdc14A by PLK1 may be involved in ß­TC3 cell cycle regulation and insulin secretion under high glucose conditions.

Application of hyperspectral imaging and chemometrics for variety classification of maize seeds.

Seed variety classification is important for assessing variety purity and increasing crop yield. A hyperspectral imaging system covering the spectral range of 874-1734 nm was applied for variety classification of maize seeds. A total of 12 900 maize seeds including 3 different varieties were evaluated. Spectral data of 975.01-1645.82 nm were extracted and preprocessed. Discriminant models were developed using a radial basis function neural network (RBFNN). The influence of calibration sample size on classification accuracy was studied. Results showed that with the expansion of calibration sample size, calibration accuracy varied slightly, but prediction accuracy changed from the increasing form to the stable form. Accordingly, the optimal size of the calibration set was determined. Optimal wavelength selection was conducted by loading of principal components (PCs). The RBFNN model developed on optimal wavelengths with the optimal size of the calibration set obtained satisfactory results, with calibration accuracy of 93.85% and prediction accuracy of 91.00%. Visualization of classification map of seed varieties was achieved by applying this RBFNN model on the average spectra of each sample. Besides, the procedure to determine the optimal sample quantity proposed in this study was verified by support vector machine (SVM). The overall results indicated that hyperspectral imaging was a potential technique for variety classification of maize seeds, and would help to develop a real-time detection system for maize seeds as well as other crop seeds.