Zhiming Guo , Xuan Chen , Chanjun Sun , Usman Majeed , Chen Wang , Shuiquan Jiang , Xiaobo Zou
{"title":"Optical properties of multilayered tissues of different varieties of apples and inspection models of internal quality","authors":"Zhiming Guo , Xuan Chen , Chanjun Sun , Usman Majeed , Chen Wang , Shuiquan Jiang , Xiaobo Zou","doi":"10.1016/j.jfca.2025.107942","DOIUrl":null,"url":null,"abstract":"<div><div>Apples being multilayered fruits possess optical properties which respond to spectroscopy-based quality inspection models. The variation in optical properties of apple tissues is crucial for efficient spectroscopic detection. Absorption coefficients (µ<sub>a</sub>) and the reduced scattering coefficients (µ'<sub>s</sub>) of three apple varieties had wavelength range from 475 nm to 1600 nm using a double integrating sphere technique. The quantitative prediction models for soluble solid content (SSC), firmness index (FI), and pH for synergistic interval (SI), competitive adaptive reweighted sampling (CARS), and genetic algorithm (GA) with partial least square (PLS) was accurate. Interestingly, CARS-PLS model using µ<sub>a</sub> provided the best quantitative predictions for SSC (Rp = 0.9833, RMSEP = 0.2630) and pH (Rp = 0.8429, RMSEP = 0.1229). Additionally, the GA-PLS model based on µ'<sub>s</sub> yield accurate prediction for FI (Rp = 0.9372, RMSEP = 0.1478). On the other hand, differences in apple peel color were also observed among the varieties. The qualitative discrimination model random forest (RF) and backpropagation (BP) for apple varieties color detection achieved highest accuracy (100 %). These findings confirmed the feasibility of combining optical properties with color detection to identify apple variety.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"146 ","pages":"Article 107942"},"PeriodicalIF":4.0000,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Food Composition and Analysis","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0889157525007574","RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CHEMISTRY, APPLIED","Score":null,"Total":0}
引用次数: 0
Abstract
Apples being multilayered fruits possess optical properties which respond to spectroscopy-based quality inspection models. The variation in optical properties of apple tissues is crucial for efficient spectroscopic detection. Absorption coefficients (µa) and the reduced scattering coefficients (µ's) of three apple varieties had wavelength range from 475 nm to 1600 nm using a double integrating sphere technique. The quantitative prediction models for soluble solid content (SSC), firmness index (FI), and pH for synergistic interval (SI), competitive adaptive reweighted sampling (CARS), and genetic algorithm (GA) with partial least square (PLS) was accurate. Interestingly, CARS-PLS model using µa provided the best quantitative predictions for SSC (Rp = 0.9833, RMSEP = 0.2630) and pH (Rp = 0.8429, RMSEP = 0.1229). Additionally, the GA-PLS model based on µ's yield accurate prediction for FI (Rp = 0.9372, RMSEP = 0.1478). On the other hand, differences in apple peel color were also observed among the varieties. The qualitative discrimination model random forest (RF) and backpropagation (BP) for apple varieties color detection achieved highest accuracy (100 %). These findings confirmed the feasibility of combining optical properties with color detection to identify apple variety.
期刊介绍:
The Journal of Food Composition and Analysis publishes manuscripts on scientific aspects of data on the chemical composition of human foods, with particular emphasis on actual data on composition of foods; analytical methods; studies on the manipulation, storage, distribution and use of food composition data; and studies on the statistics, use and distribution of such data and data systems. The Journal''s basis is nutrient composition, with increasing emphasis on bioactive non-nutrient and anti-nutrient components. Papers must provide sufficient description of the food samples, analytical methods, quality control procedures and statistical treatments of the data to permit the end users of the food composition data to evaluate the appropriateness of such data in their projects.
The Journal does not publish papers on: microbiological compounds; sensory quality; aromatics/volatiles in food and wine; essential oils; organoleptic characteristics of food; physical properties; or clinical papers and pharmacology-related papers.