Seven commercial Chinese chrysanthemum tea products were classified by computer vision combined with machine learning algorithms. Without the need of building any specific hardware, the image acquisition was achieved in two computer vision approaches. In the first approach, a series of multivariate classification models were built after morphological feature extraction of the image. The best prediction accuracies when classifying flowering stages and tea types were respectively 90% and 63%. In comparison, the deep neural network was applied directly on the raw image, yielded 96% and 89% correct identifications when classifying flowering stage and tea type, respectively. The model can be applied for rapid and automatic quality determination of teas and other related foods. The result indicated that computer vision, especially when combined with deep learning or other machine learning techniques can be a convenient and versatile method in the evaluation of food quality.

Fourier transform infrared (FTIR) spectroscopy combined with chemometrics was used to authenticate raw milk from their reconstituted counterparts. First, the explanatory principal component analysis (PCA) was employed to visualize the relationship between raw and reconstituted milk samples. However, the degree of separation between two sample classes was not significant according to direct observation of the scores plot, indicating FTIR spectra may contain complicated chemical information. Second, partial least-squares-discriminant analysis (PLS-DA) that incorporate additional class membership information as modelling input was further calculated. The PLS-DA scores yielded clear separation between two classes of samples. Additionally, possible components from the model loading were studied, and the PLS-DA model was validated internally under the model population analysis framework, as well as externally using an independent test set. This study gave insights into the authentication of milk using FTIR spectroscopy with chemometrics techniques.

Detection of the presence of milk powder in liquid whole milk is challenging due to their similar chemical components. In this study, a sensitive and robust approach has been developed and tested for potential utilization in discriminating adulterated milk from liquid whole milk by analyzing the intact protein and hydrolyzed peptide using ultra‐performance liquid chromatography with quadrupole time‐of‐flight mass spectrometer (UPLC‐QTOF‐MS) fingerprints combined with data fusion. Two different datasets from intact protein and peptide fingerprints were fused to improve the discriminating ability of principle component analysis (PCA). Furthermore, the midlevel data fusion coupled with PCA could completely distinguish liquid whole milk from the milk. The limit of detection of milk powder in liquid whole milk was 0.5% (based on the total protein equivalence). These results suggested that fused data from intact protein and peptide fingerprints created greater synergic effect in detecting milk quality, and the combination of data fusion and PCA analysis could be used for the detection of adulterated milk.