Quantification of Corn Adulteration in Wet and Dry-Processed Peaberry Ground Roasted Coffees by UV–Vis Spectroscopy and Chemometrics

Abstract

In this present research, a spectroscopic method based on UV–Vis spectroscopy is utilized to quantify the level of corn adulteration in peaberry ground roasted coffee by chemometrics. Peaberry coffee with two types of bean processing of wet and dry-processed methods was used and intentionally adulterated by corn with a 10–50% level of adulteration. UV–Vis spectral data are obtained for aqueous samples in the range between 250 and 400 nm with a 1 nm interval. Three multivariate regression methods, including partial least squares regression (PLSR), multiple linear regression (MLR), and principal component regression (PCR), are used to predict the level of corn adulteration. The result shows that all individual regression models using individual wet and dry samples are better than that of global regression models using combined wet and dry samples. The best calibration model for individual wet and dry and combined samples is obtained for the PLSR model with a coefficient of determination in the range of 0.83–0.93 and RMSE below 6% (w/w) for calibration and validation. However, the error prediction in terms of RMSEP and bias were highly increased when the individual regression model was used to predict the level of corn adulteration with differences in the bean processing method. The obtained results demonstrate that the use of the global PLSR model is better in predicting the level of corn adulteration. The error prediction for this global model is acceptable with low RMSEP and bias for both individual and combined prediction samples. The obtained RPDp and RERp in prediction for the global PLSR model are more than two and five for individual and combined samples, respectively. The proposed method using UV–Vis spectroscopy with a global PLSR model can be applied to quantify the level of corn adulteration in peaberry ground roasted coffee with different bean processing methods.

Non-Targeted Detection and Quantification of Food Adulteration of High-Quality Stingless Bee Honey (SBH) via a Portable LED-Based Fluorescence Spectroscopy

“…PLSR, PCR, and MLR models were validated by a t -test validation method to optimize the model parameters. In accordance with Yulia and Suhandy [ 29 ], several statistical parameters were used to assess the calibration model, including the coefficients of determination of calibration and validation (R 2 c and R 2 v ), root means squared errors of calibration and validation (RMSEC and RMSEV), the ratio of prediction to deviation in prediction (RPD), and the range error ratio (RER) index. The limit of detection (LOD) and limit of quantification (LOQ) were also calculated according to Milani et al [ 57 ] and Rambla-Alegre et al [ 58 ], respectively.…”

“…More recent honey authenticity detection techniques, including near-infrared (NIR), Raman, and fluorescence spectroscopy [ 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 ], also suffer from several limitations, such as being time-consuming, involving expensive benchtop-based instruments, requiring a highly-trained operator to perform the analysis, and in a number of cases generating toxic waste and using expensive chemicals. On the other hand, the use of easy, fast, and chemical-free analytical methods based on ultraviolet-visible (UV-visible) spectroscopy with a benchtop spectrometer for testing the authenticity of food has been well reported in Indonesia, especially for coffee [ 27 , 28 , 29 ], tea [ 30 ], and honey [ 31 ].…”

Recent Research Advancements of Coffee Quality Detection: Targeted Analyses vs. Nontargeted Fingerprinting and Related Issues

“…UV-Vis usually has a lower cost and wider instrument availability compared with IR. Yulia and Suhandy [33] obtained UV-Vis spectral data in the range of 250-400 nm by mixing diferent proportions of corn into cofee. PLSR, multiple linear regression (MLR), and PCR predicted the adulteration ratio of corn in cofee.…”

“…Among them, the most typical and widely addressed one may be economically motivated adulteration (EMA) [19]. Tere are three types of cofee in the market: green, roasted, and processed cofee [20], all are susceptible to being adulterated with the by-products of cofee or other plant materials, such as cofee husks [21,22], waste cofee grounds [23], brown sugar [24], acai [25], cheaper grains (wheat [26], rice [27], rye [28], barley [2,29,30], corn [26,[31][32][33], soybean [32]), and chicory [30,34]. Besides, intentionally mislabeling lower-valued beans as their higher-valued counterparts and blending different species or origins of beans are other possible forms of fraud [35,36].…”

Adulteration identification of Hainan camellia (Camellia oleifera Abel.) oil based on comprehensive two‐dimensional gas chromatography and quadrupole time‐of‐flight mass coupled with chemometrics spectrometry

“…However, this identification method relies on the unique chemical composition of fatty acids and phytosterols, which change when camellia oil is adulterated with other cheaper oils. In addition, the use of UV–Vis–NIR spectroscopy in combination with feature selection methods enables rapid and cost‐effective quantification of adulterants in camellia oil (Liu et al ., 2023). This method provides a non‐destructive and efficient way to screen for adulteration, but it may be limited in detecting certain types of adulterants due to the optical properties of the oil.…”