A novel method for the nondestructive classification of different‐age Citri Reticulatae Pericarpium based on data combination technique

• Main Authors: Pao Li, Xinxin Zhang, Yu Zheng, Fei Yang, Liwen Jiang, Xia Liu, Shenghua Ding, Yang Shan
• Format: Article
• Language: English
• Published: Wiley 2021-02-01
• Series: Food Science & Nutrition
• Subjects: chemometric method; Citri Reticulatae Pericarpium; classification; data combination; near‐infrared diffuse reflectance spectroscopy
• Online Access: https://doi.org/10.1002/fsn3.2059

Abstract The quality of Citri Reticulatae Pericarpium (CRP) is closely correlated with the aging time. However, CRPs in different storage ages are similar in appearance, and the young CRP may be labeled as the aged one to obtain the excess profit by some unscrupulous traders. Most traditional analysis methods are laborious and time‐consuming, and they can hardly realize the nondestructive classification. In this paper, a novel method based on near‐infrared diffuse reflectance spectroscopy (NIRDRS) and data combination technique for the nondestructive classification of different‐age CRPs was proposed. The CRPs in different storage ages (5, 10, 15, 20, and 25 years) were measured. The near‐infrared spectra of outer skin and inner capsule were obtained. Principal component analysis (PCA), soft independent modeling of class analogy (SIMCA), and Fisher's linear discriminant analysis (FLD), with different data pretreatment methods, were used for the classification analysis. Data combination of the outer skin and inner capsule spectra was discussed for further improving the classification results. The results show that multiple sensors provide more useful and complementary information than a single sensor does for improving the prediction accuracy. With the help of data combination strategy, 100% prediction accuracy can be obtained with both second‐order derivative–FLD and continuous wavelet transform–multiplicative scatter correction–FLD methods.