Application of near-infrared (NIR) spectroscopy for predicting changes of water content and rind colour of red delicious Washington apple (Malus domestica) in room temperature storage

• Main Authors: Agustiningrum, Dyah Ayu, Sofianto, Imran Arra’d, Damayanti, Ratih, Djarwanto, Rahmanto, R. G. Hadi, Pari, Rohmah, Dewi, Listya Mustika, Bahanawan, Adik, Triwibowo, Dimas, Darmawan, Teguh, Adi, Danang Sudarwoko, Amin, Yusup, Dwianto, Wahyu, Sudarmanto, Narto, Sejati, Prabu Satria
• Format: Conference Proceeding
• Language: English
• Subjects: Color; Infrared spectroscopy; Least squares method; Moisture content; Near infrared radiation; Organic materials; Room temperature; Spectra; Spectrum analysis
• ISSN: 0094-243X; 1551-7616
• DOI: 10.1063/5.0184485

An apples water content and rind colour affect Indonesia’s people in choosing and buying this fruit. Near­ infrared (NIR) spectroscopy was applied to predict precisely the best "before date" of fruits in industries by predicting the changes of water content and rind colour. Thirty apples of Red Delicious Washington species bought from supermarket served as research materials. Water content data were obtained by six times weighing within two months and simultaneously measured their L*, a*, and b* coordinates (CIE) for rind colour in two places using ColorReader CR-10 Plus and NIR spectrometer. 288 data changes of water content and rind colour were combined with their NIR spectra for analysis using CV-PLSR (cross­ validation partial least squares regression) as chemometrics to produce the prediction. The water content changes in the samples were 0.8%, while the rind colour changes were 5.9 (ΔE*) within two months of research. The application ofNIR to predict changes of water content and rind colour in apple resulted 0.72 and 0.42 of R2CV, respectively, as the best prediction result from their original NIR spectra data. NIR spectroscopy could predict sufficiently the changes in water content and rind colour of apple fruit in room temperature during storage. NIR spectroscopy showed more precision to predict water than the colour of organic materials.