Classification and quantification of sucrose from sugar beet and sugarcane using optical spectroscopy and chemometrics

ERİKLİOĞLU H., İLHAN E., Khodasevich M., Korolko D., Manley M., Castillo R., ...Daha Fazla

Journal of Food Science, cilt.88, sa.8, ss.3274-3286, 2023 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 88 Sayı: 8
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1111/1750-3841.16674
  • Dergi Adı: Journal of Food Science
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Agricultural & Environmental Science Database, Analytical Abstracts, Applied Science & Technology Source, Aquatic Science & Fisheries Abstracts (ASFA), BIOSIS, Biotechnology Research Abstracts, CAB Abstracts, Chemical Abstracts Core, Computer & Applied Sciences, EMBASE, Environment Index, Food Science & Technology Abstracts, INSPEC, MEDLINE, Veterinary Science Database, DIALNET
  • Sayfa Sayıları: ss.3274-3286
  • Anahtar Kelimeler: multivariate data analysis, sucrose, sugar beet, sugarcane, UV–Vis spectroscopy
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

Sucrose, obtained from either sugar beet or sugarcane, is one of the main ingredients used in the food industry. Due to the same molecular structure, chemical methods cannot distinguish sucrose from both sources. More practical and affordable methods would be valuable. Sucrose samples (cane and beet) were collected from nine countries, 25% (w/w) aqueous solutions were prepared and their absorbances recorded from 200 to 1380 nm. Spectral differences were observable in the ultraviolet–visible (UV–Vis) region from 200 to 600 nm due to impurities in sugar. Linear discriminant analysis (LDA), classification and regression trees, and soft independent modeling of class analogy were tested for the UV–Vis region. All methods showed high performance accuracies. LDA, after selection of five wavelengths, gave 100% correct classification with a simple interpretation. In addition, binary mixtures of the sugar samples were prepared for quantitative analysis by means of partial least squares regression and multiple linear regression (MLR). MLR with first derivative Savitzky–Golay were most acceptable with root mean square error of cross-validation, prediction, and the ratio of (standard error of) prediction to (standard) deviation values of 3.92%, 3.28%, and 9.46, respectively. Using UV–Vis spectra and chemometrics, the results show promise to distinguish between the two different sources of sucrose. An affordable and quick analysis method to differentiate between sugars, produced from either sugar beet or sugarcane, is suggested. This method does not involve complex chemical analysis or high-level experts and can be used in research or by industry to detect the source of the sugar which is important for some countries’ agricultural policies.