Badaró Amanda Teixeira, Garcia-Martin Juan Francisco, López-Barrera María Del Carmen, Barbin Douglas Fernandes, Alvarez-Mateos Paloma
Department of Food Engineering, University of Campinas (UNICAMP), Rua Monteiro Lobato, 80, Cidade Universitária, Campinas-SP 13083-862, Brazil.
Departamento de Ingeniería Química, Facultad de Química, Universidad de Sevilla, Sevilla 41012, Spain.
Food Chem. 2020 Apr 18;323:126861. doi: 10.1016/j.foodchem.2020.126861.
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 (R > 0.93) than those based on few selected wavelengths (R 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.
果胶在食品和制药行业有多种用途,因此对其进行定量分析对于进一步提取至关重要。目前的果胶定量技术需要使用化学物质进行提取并产生残留物。本研究使用近红外高光谱成像(NIR-HSI)来测定橙皮中的果胶含量。采集了140个含有不同果胶含量的橙皮的高光谱图像,波长范围为900 - 2500nm,并将光谱用于多元统计分析的校准模型。主成分分析(PCA)和线性判别分析(LDA)在考虑低(0 - 5%)、中(10 - 40%)、高(50 - 100%)果胶含量的三组情况下显示出较好的结果。基于全光谱的偏最小二乘回归(PLSR)模型比基于少数选定波长的模型具有更高的精度(R > 0.93)(R在0.92和0.94之间)。结果表明,NIR-HSI有潜力定量分析橙皮中的果胶含量,为橙子生产商和加工行业提供了一项有价值的技术。