ABSTRACT
Surface enhanced Raman spectroscopy (SERS) has been widely studied and recognized as a powerful label-free technique for trace chemical analysis. However, its drawback in simultaneously identifying several molecular species has greatly limited its real-world applications. In this work, we reported a combination between SERS and independent component analysis (ICA) to detect several trace antibiotics which are commonly used in aquacultures, including malachite green, furazolidone, furaltadone hydrochloride, nitrofurantoin, and nitrofurazone. The analysis results indicate that the ICA method is highly effective in decomposing the measured SERS spectra. The target antibiotics could be precisely identified when the number of components and the sign of each independent component loading were properly optimized. With SERS substrates, the optimized ICA can identify trace molecules in a mixture at a concentration of 10-6 M achieving the correlation values to the reference molecular spectra of 71-98%. Furthermore, measurement results obtained from a real-world sample demonstration could also be recognized as an important basis to suggest this method is promising for monitoring antibiotics in a real aquatic environment.
Subject(s)
Anti-Bacterial Agents , Spectrum Analysis, Raman , Spectrum Analysis, Raman/methodsABSTRACT
Caffeine, quinic acid, and nicotinic acid are among the significant chemical determinants of coffee quality. This study develops a chemometric model to quantify these compounds in ternary mixtures analyzed by terahertz time-domain spectroscopy (THz-TDS). A data set of 480 THz spectra was obtained from 80 samples. Combinations of data preprocessing methods, including normalization (Z-score, min-max scaling, Mie baseline removal) and dimensionality reduction (principal component analysis (PCA), factor analysis (FA), independent component analysis (ICA), locally linear embedding (LLE), non-negative matrix factorization (NMF), isomap), and prediction models (partial least-squares regression (PLSR), support vector regression (SVR), multilayer perceptron (MLP), convolutional neural network (CNN), gradient boosting) were analyzed for their prediction performance (totaling to 4,711,685 combinations). Results show that the highest quantification performance was achieved at a root-mean-square error of prediction (RMSEP) of 0.0254 (dimensionless mass ratio), using min-max scaling and factor analysis for data preprocessing and multilayer perceptron for prediction. Effects of preprocessing, comparison of prediction models, and linearity of data are discussed.
ABSTRACT
Lactose plays a significant role in daily lives as a constituent of various food and pharmaceutical products. Yet, lactose intolerance conditions demand low-lactose and lactose-free products in the market. These increasing nutritional claims and labels on food products entail simple and reliable methods of analysis that can be used for meeting quality standards, nutritional claims and legal requirements. In this study, terahertz time-domain spectroscopy (THz-TDS) was employed to analyse α-lactose monohydrate qualitatively and quantitatively in food products. Both absorption spectra and absorption coefficient spectra were investigated for their prediction performance. Regression models for lactose quantification using peak area and height of the absorption peaks 0.53 and 1.37 THz were developed and assessed in infant formula samples. Satisfactory prediction results were achieved in ideal conditions with pure standards, but not in all predictions of infant formula samples. Reasons and further implications are discussed.
