Different geographical origins can lead to great variance in coffee quality, taste, and commercial value. Hence, controlling the authenticity of the origin of coffee beans is of great importance for producers and consumers worldwide. In this study, terahertz (THz) spectroscopy, combined with machine learning methods, was investigated as a fast and non-destructive method to classify the geographic origin of coffee beans, comparing it with the popular machine learning methods, including convolutional neural network (CNN), linear discriminant analysis (LDA), and support vector machine (SVM) to obtain the best model. The curse of dimensionality will cause some classification methods which are struggling to train effective models. Thus, principal component analysis (PCA) and genetic algorithm (GA) were applied for LDA and SVM to create a smaller set of features. The first nine principal components (PCs) with an accumulative contribution rate of 99.9% extracted by PCA and 21 variables selected by GA were the inputs of LDA and SVM models. The results demonstrate that the excellent classification (accuracy was 90% in a prediction set) could be achieved using a CNN method. The results also indicate variable selecting as an important step to create an accurate and robust discrimination model. The performances of LDA and SVM algorithms could be improved with spectral features extracted by PCA and GA. The GA-SVM has achieved 75% accuracy in a prediction set, while the SVM and PCA-SVM have achieved 50 and 65% accuracy, respectively. These results demonstrate that THz spectroscopy, together with machine learning methods, is an effective and satisfactory approach for classifying geographical origins of coffee beans, suggesting the techniques to tap the potential application of deep learning in the authenticity of agricultural products while expanding the application of THz spectroscopy.

In this paper, the effects of two-dimensional correlation spectroscopy (2DCOS) on chance correlations in the spectral data, generated from the correlations between glucose concentration and some undesirable experimental factors, such as instrument drift, sample temperature variations, and interferent compositions in the sample matrix, are investigated. The aim is to evaluate the validity of the spectral data set, instead of assessing the calibration models, and then to provide a complementary procedure for better verifying or rejecting the data set. It includes tracing back to the source of the chance correlation on the chemical basis, selecting appropriate preprocessing methods before building multivariate calibration models, and therefore may avoid invalid models. The utility of the proposed analysis is demonstrated with a series of aqueous solutions using near-infrared spectra over the overtone band of glucose. Results show that, spectral variations from chance correlations induced by those experimental factors can be determined by the 2DCOS method, which develops avenues for prospectively accurate prediction in clinical application of this technology.

The detection of the storage state of frozen meat, especially meat frozen–thawed several times, has always been important for food safety inspections. Hyperspectral imaging (HSI) is widely applied to detect the freshness and quality of meat or meat products. This study investigated the feasibility of the low-cost HSI system, combined with the chemometrics method, to classify beef cuts among fresh (F), frozen–stored (F–S), frozen–thawed three times (F–T–3) and frozen–thawed five times (F–T–5). A compact, low-cost HSI system was designed and calibrated for beef sample measurement. The classification model was developed for meat analysis with a method to distinguish fat and muscle, a CARS algorithm to extract the optimal wavelength subset and three classifiers to identify each beef cut among different freezing processes. The results demonstrated that classification models based on feature variables extracted from differentiated tissue spectra achieved better performances, with ACCs of 92.75% for PLS-DA, 97.83% for SVM and 95.03% for BP-ANN. A visualization map was proposed to provide detailed information about the changes in freshness of beef cuts after freeze–thawing. Furthermore, this study demonstrated the potential of implementing a reasonably priced HSI system in the food industry.

A new discriminant method to classify adulterated milk and pure milk is proposed by combining the parameterization of two-dimensional (2D) correlation spectroscopy with least squares support vector machines (LS-SVM). 120 pure milk samples and 120 milk samples adulterated with melamine, urea and glucose were prepared and their synchronous 2D correlation spectra were calculated. Then 5 statistical parameters, which were mean, variance, standard deviation, skewness and kurtosis, were extracted based on the parameterization theory. Finally, the discriminant model of adulterated milk and pure milk was built combining these statistical parameters with LS-SVM. The ratios of correct classification 96.3% and 90% for calibration set and prediction set, respectively, were obtained. The results show that this method can not only extract effectively feature information of adulterant in milk, but also reduce the input dimension of LS-SVM and computational time demands, and so better fitted to realize discriminant analysis of adulterated milk and pure milk.

A new method for discrimination analysis of adulterated milk and pure milk is proposed by combining two-dimensional correlation spectroscopy (2D-COS) with kernel orthogonal projection to latent structure (K-OPLS). Three adulteration types of milk with urea, melamine, and glucose were prepared, respectively. The synchronous 2D spectra of adulterated milk and pure milk samples were calculated. Based on the characteristics of 2D correlation spectra of adulterated milk and pure milk, a discriminant model of urea-tainted milk, melamine-tainted milk, glucose-tainted milk, and pure milk was built by K-OPLS. The classification accuracy rates of unknown samples were 85.7, 92.3, 100, and 87.5%, respectively. The results show that this method has great potential in the rapid discrimination analysis of adulterated milk and pure milk.