Determination of pectin content in orange peels by near infrared hyperspectral imaging

• Published in: Food chemistry Vol. 323; p. 126861
• Main Authors: Badaró, Amanda Teixeira, Garcia-Martin, Juan Francisco, López-Barrera, María del Carmen, Barbin, Douglas Fernandes, Alvarez-Mateos, Paloma
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.09.2020
• Subjects: Agriculture; Linear discriminant analysis; Partial least squares regression, near infrared spectra; Principal component analysis; Agriculture; Partial least squares regression, near infrared spectra; Linear discriminant analysis; Principal component analysis
• ISSN: 0308-8146; 1873-7072
• DOI: 10.1016/j.foodchem.2020.126861

•We classified orange peel samples according to their pectin content.•Hyperspectral imaging was investigated for quantification of pectin in orange peels.•We identified optimum wavelengths in spectral range.•We compared full spectra and reduced models for pectin quantification. Pectin has several purposes in the food and pharmaceutical industry making its quantification important for further extraction. Current techniques for pectin quantification require its extraction using chemicals and producing residues. Determination of pectin content in orange peels was investigated using near infrared hyperspectral imaging (NIR-HSI). Hyperspectral images from orange peel (140 samples) with different amounts of pectin were acquired in the range of 900–2500 nm, and the spectra was used for calibration models using multivariate statistical analyses. Principal component analysis (PCA) and linear discriminant analysis (LDA) showed better results considering three groups: low (0–5%), intermediate (10–40%) and high (50–100%) pectin content. Partial least squares regression (PLSR) models based on full spectra showed higher precision (R2 > 0.93) than those based on few selected wavelengths (R2 between 0.92 and 0.94). The results demonstrate the potential of NIR-HSI to quantify pectin content in orange peels, providing a valuable technique for orange producers and processing industries.