Non-alcoholic beverages are subject to adulteration in many different forms. Sugar-based drinks, especially fruit juice, are valuable and can be diluted with water or adulterated with cheaper sugar or saccharin. High carbohydrate foods and beverages can be difficult for adulteration detection because of similarities in carbohydrate profiles with commercial sweeteners. Dilution with sugar water and citric acid can be undetectable by standard tests. Purity assessments are important in all forms of non-alcoholic beverages. The coffee market has especially grown in recent years and there are many potential adulterants of coffee, such as corn, sticks, coffee husks, and in the case of Arabica which is the most valuable coffee bean, a cheaper variety of coffee. Current quality tests are often insufficient for proper adulterant detection as well as being expensive and time-consuming. NIR spectroscopy has been examined as a method for detection of adulterants in non-alcoholic beverages and the results of some studies are shown below.


Products: Fruit Juice, Lime Juice, Coffee Adulterants: Saccharin, Natural vs. Synthetic, Corn

Scientific References and Statistics

Applications of FT-NIRS Combined With PLS Multivariate Methods For the Detection & Quantification of Saccharin Adulteration in Commercial Fruit Juices – Mabood, Hussain, Jabeen, Food Additives and Contaminants: Part A, 2018, Vol. 35, No. 6, 1052-1060 Detecting adulteration in foods that are high in carbohydrates can be difficult because there are a variety of commercial sweeteners that match the concentration profile of major carbohydrates. One potential method for detecting commercial sweetener adulterants in fruit juice is NIR spectroscopy and this study examined detecting and quantifying saccharin adulteration for this purpose. Six different commercial fruit juices were obtained for the study. Each sample was spiked with saccharin ranging from 0.10% to 2.00% w/v at varying intervals. The pure samples with 0% saccharin were used as well and in total, one hundred ninety-eight samples were used. Eighteen were pure juice samples and the remainder were spiked with saccharin. FT-NIR spectra were collected for all samples from 10000 cm-1 to 4000 cm-1 using a sealed cell at 0.20 mm pathlength and 2 cm-1 resolution. Various pretreatments were performed on the spectral data for model optimization. Three separate modeling algorithms were used: Principle Component Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA) for classifying adulterated and non-adulterated samples and Partial Least Squares (PLS) for quantifying amount of saccharin present based on the NIR spectra and reference values for saccharin.

PLS-DA Classification:

R² = 0.979RMSEP= 0.067


R² = 0.970RMSEP= 0.88% of 0% to 2% Saccharin range (w/v)
Visual examination of the spectra showed a clear difference between the samples with saccharin present and 0% saccharin. PCA first showed a clear grouping between these two sets and in order to confirm the validity of determining the presence of saccharin from the NIR spectra, a PLS-DA model was created. This algorithm assigns two arbitrary values (0 and 1 in this case) to separate data sets and predicts a value for classification purposes. The high correlation and low RMSEP prove that this calibration could be used to determine the presence of saccharin in fruit juice. The PLS model performed a quantitative measurement measuring the amount of saccharin present in the samples based on the NIR spectra and reference values. Correlation was high and a validation data set proved the feasibility of the calibration. The results were especially good considering the different types of fruit juice samples used in the study and provide a basis for using a universal model with many types of fruit juice to identify and quantify the presence of saccharin adulterant using NIR spectroscopy. Combined Data Mining/NIR Spectroscopy for Purity Assessment of Lime Juice – Shafiee, Minaei, Infrared Physics and Technology 91 (2018) 193-199 Adulteration of fruit juice is a common practice and is always evolving to avoid detection. The major components of fruit juice are water, sugars, and organic acids with lesser amounts of amino acids, vitamins, and phenolic compounds. The demand for lime juice for both direct consumption and as a cooking supplement has led to various types of adulteration, even including completely synthetic products which can potentially result in health hazards for the consumer. While acidity and sugar measurements can detect dilution with water, which is the simplest form of adulteration, it is becoming more common to use water containing sugar and citric acid to avoid detection by simple tests. There is a need for a simple, fast, and cost-effective method to determine whether a lime juice sample is natural or synthetic and NIR spectroscopy was examined for this purpose. Thirty-four pure lime juice samples were procured from different orchards by picking lime fruit and using a juicer machine and filtration. Thirty-eight samples of synthetic lime juice were purchased from a market and subjected to quality assessment in the laboratory to confirm their synthetic nature. Samples were scanned from 350 nm to 2500 nm at 1 nm intervals and averaging twenty-five scans per spectrum. Spectral resolution at 1000 nm was approximately 3 nm. This process was repeated six times for each sample and the six spectra were then averaged into one spectrum per sample. Various pretreatments were performed on the spectral data for model optimization. Different classification algorithms and data mining techniques were applied to the spectra to determine the feasibility of classifying natural and synthetic lime juice.
Principle Component Analysis (PCA)Classification Accuracy 66%
Support Vector Machine (SVM)Classification Accuracy 97%
Genetic Algorithm (GA)Classification Accuracy 93%
PCA is the standard chemometric algorithm for classification analysis and to determine any outliers in a group of samples. Cross validation tests using the generated PCA model showed a poor grouping between the two sets of samples. SVM is a more advanced data mining algorithm that analyzes specific features in the data for deviation reduction and uses selective areas for input to perform a classification analysis on the data. GA is a variable selection method that is based on principles of natural and genetic selection of the data. In this case, the SVM algorithm tested a 97% accuracy rate when analyzing the spectra of both data sets for classification purposes. The study verified the capability of using NIR spectroscopy in combination with data mining and powerful classification methods to accurately discriminate between natural and synthetic lime juice.


Detection of Corn Adulteration in Brazilian Coffee (Coffea Arabica) by Tocopherol Profiling and Near-Infrared (NIR) Spectroscopy – Winkler-Moser, Singh, Rennick, et al., Journal of Agricultural and Food Chemistry, 2015, 63, 10662-10668 Coffee is considered a high-value commodity that is a frequent target for adulteration, as are many other food and beverage products. There is significant interest in developing and improving methods for detecting coffee adulteration and one such method that has been examined is NIR spectroscopy. Potential adulterants for coffee include corn, sticks, coffee husks, and other lower value crops. Fourteen different lots of green Arabica coffee beans from different cities and plantations in Brazil were procured for the study. All coffee samples were roasted as well as corn to be added as an adulterant. Both the coffee and corn were ground before mixing. Five different concentrations of corn adulterant were added and mixed with each sample ranging from 1% to 20% corn. In addition, the pure Arabica sample from each lot was used as well, making for a total of eighty-four samples. Samples were scanned using an NIR spectrometer from 400 nm to 2500 nm. Tocopherol content was chosen as a basis for determining the percentage of corn adulteration and reference values for tocopherol in the samples were determined using HPLC. The HPLC results and a Multiple Linear Regression (MLR) equation were used to correlate the tocopherol profile results to the percentage of corn adulteration. This correlation was then used as a reference value to correlate the NIR spectra to % corn adulteration using Partial Least Squares (PLS) analysis.
% Corn AdulterantR² = 0.986RMSEP = 1.171%
Results of the calibration model showed good correlation and prediction values proved the feasibility of the model. Both the NIR and HPLC methods showed comparable results with about a 5% sensitivity, proving the potential of NIR spectroscopy as a fast, simple, and reliable method for detecting corn adulterant in ground coffee. The suggested next step is further study incorporating different species of coffee with other kinds of adulterants to test the feasibility of a universal model for adulteration. Such a model would have to be continuously updated with new data as different types of adulterants are emerging all the time in the world food and beverage market.