Masithoh Rudiati Evi, Lohumi Santosh, Yoon Won-Seob, Amanah Hanim Z, Cho Byoung-Kwan
Department of Agricultural and Biosystems Engineering, Faculty of Agricultural Technology, Universitas Gadjah Mada, Yogyakarta, 55281, Indonesia.
Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseoung-gu, Daejeon, 305-764, Republic of Korea.
Heliyon. 2020 Oct 22;6(10):e05099. doi: 10.1016/j.heliyon.2020.e05099. eCollection 2020 Oct.
The objective of this study was to quantify the chemical content of multiple products using one single calibration model. This study involved seven tuber and root powders from arrowroot, , cassava, taro, as well as purple, yellow, and white sweet potato, for partial least square (PLS) regression to predict polysaccharide contents (i.e., amylose, starch, and cellulose). The developed PLS models showed acceptable results, with R of 0.9, 0.95, and 0.85 and SEC of 2.7%, 3.33%, and 3.22%, for amylose, starch, and cellulose, respectively. The models also successfully predicted polysaccharide contents with R of 0.89, 0.95, and 0.79; SEP of 2.83%, 3.33%, and 3.55%; and RPD of 3.02, 4.47, and 2.18 for amylose, starch, and cellulose, respectively. These results showed the potential of Fourier transform near-infrared spectroscopy to quantify the chemical composition of multiple products instead of using one individual model.
本研究的目的是使用单一校准模型对多种产品的化学成分进行定量分析。本研究涉及来自竹芋、木薯、芋头以及紫薯、黄薯和白薯的七种块茎和根粉，用于偏最小二乘（PLS）回归以预测多糖含量（即直链淀粉、淀粉和纤维素）。所建立的PLS模型显示出可接受的结果，直链淀粉、淀粉和纤维素的R分别为0.9、0.95和0.85，SEC分别为2.7%、3.33%和3.22%。这些模型还成功预测了多糖含量，直链淀粉、淀粉和纤维素的R分别为0.89、0.95和0.79；SEP分别为2.83%、3.33%和3.55%；RPD分别为3.02、4.47和2.18。这些结果表明，傅里叶变换近红外光谱法有潜力对多种产品的化学成分进行定量分析，而不是使用单个模型。
