Classification of Apricot Varieties by Infrared Spectroscopy and Machine Learning.

ABSTRACT: This work aimed to investigate using ATR-FTIR spectroscopy combined with machine learning to classify eight apricot varieties. Traditionally, variety identification relies on physicochemical property measurements, which are time-consuming and require laboratory analysis. Instead, we used the ATR-FTIR spectra from 731 apricots divided into calibration (512) and test (219) sets and three machine learning models (i.e., partial least-squares-discriminant analysis (PLS-DA), support vector machine (SVM), and random forest (RF)) to accurately predict 97% of the test samples. Additionally, careful inspection of the PLS-DA regression vectors revealed a strong correlation between the spectra and biochemical composition in sugar and organic acids, validating ATR-FTIR spectroscopy as a viable alternat

SUBMITTER: Bejar-Grimalt J 

PROVIDER: S-EPMC12309246 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature