ArticleFoods (Basel, Switzerland)2021
Nondestructive Detection of Weight Loss Rate, Surface Color, Vitamin C Content, and Firmness in Mini-Chinese Cabbage with Nanopackaging by Fourier Transform-Near Infrared Spectroscopy.
Article in Foods (Basel, Switzerland), 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, 23 citations in OpenAlex.
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- Insights of freshness phenotype detection for postharvest fruit and vegetables.Plant phenomics (Washington, D.C.) · 2025Review
- Comparative Analysis of Chilling Injury in Banana Fruit During Storage: Physicochemical and Microstructural Changes, and Early Optical-Based Nondestructive Identification.Foods (Basel, Switzerland) · 2025Article
- Comparative metabolomic profiling and biomarker identification reveal nutritional and antioxidant differences betweenFood chemistry: X · 2025Article
- The Role of Near-Infrared Spectroscopy in Food Quality Assurance: A Review of the Past Two Decades.Foods (Basel, Switzerland) · 2024Review
- Simultaneous determination of pigments of spinach (Food chemistry: X · 2024Article
- Study on Black Spot Disease Detection and Pathogenic Process Visualization on Winter Jujubes Using Hyperspectral Imaging System.Foods (Basel, Switzerland) · 2023Article
- Intelligent Evaluation of Stone Cell Content of Korla Fragrant Pears by Vis/NIR Reflection Spectroscopy.Foods (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
9 authors at 4 institutions in 1 country.
Funding
Abstract
A nondestructive optical method is described for the quality assessment of mini-Chinese cabbage with nanopackaging during its storage, using Fourier transform-near infrared (FT-NIR) spectroscopy. The sample quality attributes measured included weight loss rate, surface color index, vitamin C content, and firmness. The level of freshness of the mini-Chinese cabbage during storage was divided into three categories. Partial least squares regression (PLSR) and the least squares support vector machine were applied to spectral datasets in order to develop prediction models for each quality attribute. For a comparative analysis of performance, the five preprocessing methods applied were standard normal variable (SNV), first derivative (lst), second derivative (2nd), multiplicative scattering correction (MSC), and auto scale. The SNV-PLSR model exhibited the best prediction performance for weight loss rate (
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