ArticleFood science & nutrition2021
A novel method for the nondestructive classification of different-age Citri Reticulatae Pericarpium based on data combination technique.
Article in Food science & nutrition, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- CounterfeitFood science & nutrition · 2026Article
- A convenient method for the accurate identification of Citri Reticulatae Pericarpium using image and multi-stream.PloS one · 2026Article
- Accurate Discrimination of Mold-DamagedFoods (Basel, Switzerland) · 2024Article
- Quality Change of Citri Reticulatae Pericarpium (Pericarps ofFoods (Basel, Switzerland) · 2024Article
- Evaluation of chemical components and quality in Xinhui Chenpi (Food science & nutrition · 2024Article
- Application of flash GC e-nose and FT-NIR combined with deep learning algorithm in preventing age fraud and quality evaluation of pericarpium citri reticulatae.Food chemistry: X · 2024Article
- Protected Geographical Indication Discrimination of Zhejiang and Non-ZhejiangMolecules (Basel, Switzerland) · 2023Article
- A novel method for the nondestructive classification of different-age Citri Reticulatae Pericarpium based on data combination technique.Food science & nutrition · 2021Article
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8 authors.
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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.
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