Comparison of Multiple NIR Spectrometers for Detecting Low-Concentration Nitrogen-Based Adulteration in Protein Powders.

Protein adulteration is a common fraud in the food industry due to the high price of protein sources and their limited availability. Total nitrogen determination is the standard analytical technique for quality control, which is incapable of distinguishing between protein nitrogen and nitrogen from non-protein sources. Three benchtops and one handheld near-infrared spectrometer (NIRS) with different signal processing techniques (grating, Fourier transform, and MEM-micro-electro-mechanical system) were compared with detect adulteration in protein powders at low concentration levels. Whey, beef, and pea protein powders were mixed with a different combination and concentration of high nitrogen content compounds-namely melamine, urea, taurine, and glycine-resulting in a total of 819 samples. NIRS, combined with chemometric tools and various spectral preprocessing techniques, was used to predict adulterant concentrations, while the limit of detection (LOD) and limit of quantification (LOQ) were also assessed to further evaluate instrument performance. Out of all devices and measurement methods compared, the most accurate predictive models were built based on the dataset acquired with a grating benchtop spectrophotometer, reaching R2P values of 0.96 and proximating the 0.1% LOD for melamine and urea. Results imply the possibility of using NIRS combined with chemometrics as a generalized quality control tool for protein powders.

Assessment of Maturity of Plum Samples Using Fourier Transform Near-Infrared Technique Combined with Chemometric Methods.

The FT-NIR technique was used for rapid and non-destructive determination of plum ripeness. The dry matter (DM), titratable acidity (TA), total soluble solids (TSS) and calculated maturity index (MI: TSS/TA) were used as reference values. The PLS correlations were validated via five-fold cross-validation (RMSECV for different parameters: DM: 0.66%, w/w; TA = 0.07%, w/w; TSS = 0.72%, w/w; MI = 1.39) and test set validation (RMSEP for different parameters: DM: 0.65%, w/w TA = 0.07%, w/w; TSS = 0.61%, w/w; MI = 1.50). Different classification algorithms were performed for TA, TSS and MI. Linear, quadratic and Mahalanobis discriminant analysis (LDA, QDA, MDA) were found to be the best sample detection methods. The accuracy of the classification methods was 100% for all investigated parameters and cultivars.

Analysis of wheat flour-insect powder mixtures based on their near infrared spectra.

Insects are gaining more and more space in food and feed sectors, creating an intense scientific interest towards insects as food ingredients. Several papers deal with cereal-based products complemented by insect powder in the past few years. However, adulteration and quality control of such products present some hot topics for researchers, e.g., how can we justify the amounts and/or species of the insects used in the given products? Our paper aims to answer such questions by analysing seven edible insect powders of different species independently. The mixtures with wheat flour were analysed using near infrared spectroscopy and chemometric methods. Not only powders of different species were clearly differentiated, but also mixtures created by different amounts of wheat flour. Prediction of insect content showed 0.65% cross-validated error. The proposed methodology gives an excellent tool for quality control of insect-based cereal food products.

Application of Near-Infrared Spectroscopy to Investigate Some Endogenic Properties of Pleurotus ostreatus Cultivars.

Fourteen different Pleurotus ostreatus cultivars (Po_1-Po_14) were tested for free amino acid content (fAA), total polyphenol content (TPC), and antioxidant capacity (Ferric Reducing Ability of Plasma-FRAP) to select the cultivars with the most favorable traits. Automatic amino acid analyzer (fAA) and spectrophotometric assay (TPC, FRAP) results as well as Fourier-transform near infrared (FT-NIR) spectra were evaluated with different chemometric methods (Kruskal-Wallis test, Principal Component Analysis-PCA, Linear Discriminant Analysis-LDA). Based on total free amino acid concentrations and FRAP values, the Po_2 cultivar was found to be the most favorable. Types Po_3, Po_8, Po_10 and Po_12 were separated using PCA. Based on the spectral profile, they may contain polyphenols and reducing compounds of different qualities. LDA classification that was based on the concentrations of all free amino acids, cysteine, and proline of the cultivars was performed with an accuracy of over 90%. LDA classification that was based on the TPC and FRAP values was performed with an accuracy of over 83%.

Quantification of nitrate content with FT-NIR technique in lettuce (Lactuca sativa L.) variety types: a statistical approach.

According to the Commission Regulation (EC) No. 1258/2011, the maximum allowed nitrate content of lettuce is defined within a broad range (2000-5000 mg NO3/kg), depending on harvest season and technology. This study focuses on the identification of the differences in nitrate accumulation between lettuce types and varieties, depending on production technology and on the investigation of the application of non-destructive FT-NIR spectroscopy for nitrate quantification, towards widely used UV-Vis spectroscopy. In the present study, combinations of seasons and technologies (spring × greenhouse, autumn × open field) were employed for the production of types (batavia, butterhead, lollo and oak leaf; both red and green colored); a total of 266 lettuce heads were analyzed. It was found that with standardized technology and conditions, autumn harvested green oak leaf lettuce types accumulated significantly less nitrate, than red oak or lollo leaf types. With spring harvested lettuces, batavia types generally accumulated generally more nitrates than butterhead types. Based on the linear discriminant analysis (LDA) of FT-NIR measurements the four distinct variety types diverge; the lollo type explicitly diverges from batavia and butterhead types. The LDA further revealed, that within lollo and oak leaf variety types, red and green leaved varieties diverge as well. A model was successfully built for the FT-NIR quantification of the nitrate content of lettuce samples (R2 = 0.95; RMSEE = 74.4 mg/kg fresh weight; Q2 = 0.90; RMSECV = 99.4 mg/kg fresh weight). The developed model is capable of the execution of a fast and non-invasive measurement; the method is suitable for the routine measurement of nitrate content in lettuce.

Quantitative determination and classification of energy drinks using near-infrared spectroscopy.

Almost a hundred commercially available energy drink samples from Hungary, Slovakia, and Greece were collected for the quantitative determination of their caffeine and sugar content with FT-NIR spectroscopy and high-performance liquid chromatography (HPLC). Calibration models were built with partial least-squares regression (PLSR). An HPLC-UV method was used to measure the reference values for caffeine content, while sugar contents were measured with the Schoorl method. Both the nominal sugar content (as indicated on the cans) and the measured sugar concentration were used as references. Although the Schoorl method has larger error and bias, appropriate models could be developed using both references. The validation of the models was based on sevenfold cross-validation and external validation. FT-NIR analysis is a good candidate to replace the HPLC-UV method, because it is much cheaper than any chromatographic method, while it is also more time-efficient. The combination of FT-NIR with multidimensional chemometric techniques like PLSR can be a good option for the detection of low caffeine concentrations in energy drinks. Moreover, three types of energy drinks that contain (i) taurine, (ii) arginine, and (iii) none of these two components were classified correctly using principal component analysis and linear discriminant analysis. Such classifications are important for the detection of adulterated samples and for quality control, as well. In this case, more than a hundred samples were used for the evaluation. The classification was validated with cross-validation and several randomization tests (X-scrambling). Graphical Abstract The way of energy drinks from cans to appropriate chemometric models.

Quantitative determination of coenzyme Q10 from dietary supplements by FT-NIR spectroscopy and statistical analysis.

A novel, time- and money-sparing method has been developed and validated for the quantitative determination of coenzyme Q10 (CoQ10) from several dietary supplements. FT-NIR spectroscopy was applied for the examination, and a calibration model was built by partial least-square regression (PLS-R) using 50 dietary supplements. The combination of FT-NIRS and multivariate calibration methods is a very fast and simple way to replace the commonly used HPLC-UV method; because in contrast with the traditional techniques, sample pretreatment and reagents are not required and no wastes are produced. The calibration models could be improved by different variable selection techniques (for instance interval PLS, interval selectivity ratio, genetic algorithm), which are very fast and user-friendly. The R(2) (goodness of calibration) and Q(2) (goodness of validation) of the variable selected models are highly increased, the R(2) values being over 0.90 and the Q(2) values being over 0.86 in every case. Fivefold cross-validation and external validation were applied. The developed method(s) could be used by quality assurance laboratories for routine measurement of coenzyme Q10 products.