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•A methodology was developed to identify and quantify adulterations in extra virgin olive oils (EVOO).•A portable near-infrared spectrometer (MicroNIR) was used.•Binary blends of EVOO ...with soybean, sunflower, corn, and canola oils were constructed.•The data obtained were used in the construction of PCA and PLS models.•The PLS model presented a R2 higher than 0.98.•The RMSEP values in the two spectral acquisition modes were ever lower than 5 wt%.
Olive oil is an important food product for human health. The addition of vegetable oils is the most common form of oil adulteration. In this paper, a methodology was developed to identify and quantify adulterations in extra virgin olive oil (EVOO) using a portable near-infrared spectrometer (microNIR). Two different spectral acquisition modes were tested: reflectance and transmittance. Samples sets constitute binary blends of EVOO with soybean, sunflower, corn, and canola oil. Partial least squares regression (PLS) models were built to quantify adulterations in extra virgin olive oil. After, the soybean oil content was checked in 12 olive oils acquired in a local market. Also, the physicochemical properties: acidity, peroxide index, and ultraviolet absorbance of the commercial samples were determined following the standard method of Institute Adolfo Lutz and CEE n. 2568/91. The PLS models' accuracy was 0.5 to 1.8 wt% for transmittance mode and 1.7 to 4.6 wt% for reflectance mode. The commercial olive samples' adulterations, the binary blends of commercial samples, the vegetable oils, and the binary blends EVOO/vegetable oils were evaluated by principal component analysis (PCA) and soft independent modeling class analogy (SIMCA). PCA and SIMCA models distinguished the commercial samples according to the information contained in their labels; besides, it identified the olive oil samples on to dataset with the blends. These results are in excellent agreement with the physicochemical results that corroborate with the limits of regulation.
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•-Three oleogels were prepared, containing 95% oil (sunflower, soy, olive) and 5% beeswax.•-The DD-SIMCA and PLS-DA models demonstrated 95% to 100% of accuracy.•-Handheld and portable ...instruments are promising for classifying oils and oleogels.•-The addition of beeswax maintained the peroxide values in the oleogels.
Oleogel represents a promising healthier alternative to act as a substitute for conventional fat in various food products. Oil selection is a crucial factor in determining the technological properties and applications of oleogels due to their distinct fatty acid composition, molecular weight, and thermal properties, as well as the presence of antioxidants and oxidative stability. Hence, the relevance of monitoring oleogel properties by non-destructive, eco-friendly, portable, fast, and effective techniques is a relevant task and constitutes an advance in the evaluation of oleogels quality. Thus, the present study aims to classify oleogels rapidly and reliably, without the use of chemicals, comparing two handheld near infrared (NIR) spectrometers and one portable Raman device. Furthermore, two different multivariate methods are compared for oleogel classification according to oil type. Three types of oleogels were prepared, containing 95 % oil (sunflower, soy, olive) and 5 % beeswax as a structuring agent, melted at 90 °C. Polarized light microscopy (PLM) images were acquired, and fatty acid composition, peroxide index and free fatty acid content were determined using official methods. A total of 240 oleogel and 92 oil spectra were obtained for each instrument. After spectra pretreatment, Principal Component Analysis (PCA) was performed, and two classification methods were investigated. The Data Driven - Soft Independent Modelling of Class Analogy (DD-SIMCA) and Partial Least Squares Discriminant Analysis (PLS-DA) models demonstrated 95 % to 100 % of accuracy for the external test set. In conclusion, the use of vibrational spectroscopy using handheld and portable instruments in tandem with chemometrics showed to be an efficient alternative for classifying oils and oleogels and could be extended to other food samples. Although the classification of vegetable oils by NIR is widely used and known, this work proposes the classification of different types of oil in oleogel matrices, which has not yet been explored in the literature.
Caffeine and catechin are two main components of instant green tea, and are essential components of tea quality. This paper mainly focuses on the feasibility of rapidly determining instant green tea ...components by using a portable near infrared (NIR) spectrometer. The two main components (caffeine and catechin) were studied. In addition, the instrument performance levels of portable and benchtop NIR spectrometers were studied and compared. Quantitative models developed using portable and benchtop spectrometers for measuring caffeine, total catechins, and four individual catechins were established and compared. After preprocessing using standard normal variate (SNV), the Rp values of the caffeine, total catechins, (−)-epigallocatechin, (−)-epigallocatechin 3-gallate, (−)-epicatechin, and (−)-epicatechin gallate in the partial least squares models for a portable NIR spectrometer were 0.974, 0.962, 0.669, 0.945, 0.942 and 0.905, respectively. For a benchtop NIR spectrometer, Rp values were 0.993, 0.958, 0.883, 0.955, 0.966 and 0.936, respectively. Passing-Bablok regression method results indicated no significant differences between the two instruments. A genetic algorithm (GA) and the successive projections algorithm (SPA) were used to screen the wavelength of the NIR spectrum and establish the model. The GA obtained more robust modeling results. This study concludes that the developed portable spectroscopy system combined with appropriate variable selection methods can be effectively used for rapid determination of caffeine, total catechins, and four individual catechins in instant green tea.
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•Established a rapid and nondestructive testing method for instant green tea•Evaluated feasibility of the portable NIR spectrometer compared to benchtop instruments•Optimized modeling process by applying spectral pretreatment and variable selection•Provided a novel method for the mass production and quality control of instant green tea
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•Pupunha flour is a promising alternative for cookie production.•Micro-NIR obtained excellent predictions for phenolic compound content.•Micro-NIR represents a great advantage when ...applied in industrial environments.•Even with narrow spectral range, excellent results were obtained.•Pupunha flour is an alternative product aimed at the celiac public.
There are several reports of the potential benefits of phenolic compound (PC) in food products, due to their antioxidant activities (AC). However, in recent years, new research results have demonstrated that PC has potential health risks due to the reduction in absorption of protein nutrients and cytotoxic effects. The PC and AC quantifications are laborious and time-consuming methods, therefore it is necessary to develop simple, fast and precise method to determine these parameters, not only in the raw materials, but also in food products. Therefore, this study focused on the potential of Micro-NIR spectrometer data modeled with partial least square regression to predict PC and AC in processed food (cookies) prepared with peach palm (PP), that is rich in PC. The cookies were prepared using 12.5 to 100 % of PP flour in substitution to wheat flour (WF). The NIR model for AC, determined by the ferric reducing antioxidant power (FRAP) method, shows R2cv = 0.93 (regression coefficient of cross-validation step); RMSECV = 0.05; R2p = 0.87 (regression coefficient of prediction step); RMSEP = 0.04; RPD = 2.73, and by 2,2-azinobis (3-ethylbenzothiazoline-6-sulfonic acid) radical capture (ABTS) exhibit R2cv = 0.83; RMSECV = 3.72; R2p = 0.70; RMSEP = 4.12; RPD = 1.76, and for PC, determined by Folin-Ciocalteu, shows R2cv = 0.86; RMSECV = 0.44; R2p = 0.80; RMSEP = 0.43; RPD = 2.04. These excellent results, mainly for FRAP and PC, demonstrated that portable NIR spectrometers could be a fast, simple and reliable method to predict PC and AC in cookies prepared with different proportion of PP flour and WF. Similar models can also be developed to predict PC and AC in other food products.
For the first time, the portable NIR spectrometer combined LDA and PLS two different chemometrics methods to detect the variety, authenticity and internal chemical composition of Fritillariae ...cirrhosae.
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•Rapid analysis of components and categories of Fritillaria cirrhosae by portable NIR spectrometer.•The effects of various spectral pretreatment methods on the model performance were compared.•The results showed the developed models were robust and accurate.
This paper mainly focuses on the feasibility of rapidly identifying Fritillariae cirrhosae varieties, distinguishing its authenticity and detecting its components by using a portable near infrared (NIR) spectrometer. Five different varieties of Fritillariae cirrhosae, five common counterfeits and two main components (ethanol-soluble extractives and total alkaloids) were studied. The reference values of ethanol-soluble extractives were determined by hot dip method and the reference value of total alkaloid was determined by ultraviolet–visible spectrophotometry (UV–Vis). Linear discriminant analysis (LDA) algorithm was used to identify the sources of different varieties of Fritillariae cirrhosae and the common counterfeits of Fritillariae cirrhosae, respectively. As a result, the best models seemed to be effective, with accuracy of the two models' prediction sets reaches 83.33% and 90.91%, respectively. The partial least squares regression (PLSR) algorithm was used to relate the sample spectra with the reference values of ethanol-soluble extractives and total alkaloid content. Coefficient of determination of prediction (R2p) and root mean square errors of prediction (RMSEP) obtained were 0.8562 and 0.3911; 0.6917 and 0.0117, for ethanol-soluble extractives and total alkaloid content, respectively. The results showed that the portable NIR spectrometer could evaluate the quality of Fritillariae cirrhosae with high efficiency and practicability.
Portable near-infrared (NIR) analyzer for classifying the high oleic acid peanuts (HOP) and quantitation of its major fatty acids was assessed for the first time in comparison with the benchtop NIR. ...Reference chemical values of fatty acids were calculated by the gas chromatographic method. The processed datasets were explored by principal component analysis and classification models were built by using partial least square discriminant analysis. The results showed that the accuracy of distinction of the HOP from others was 100%. Partial least square was used to build quantitative models for quantifying the peanuts’ major fatty acids. The R of the calibration model noted for the portable NIR was 0.90, 0.88 and 0.88 for oleic acid, linoleic acid and palmitic acid of the HOP with a SEC of 0.97, 0.12 and 0.12, respectively. The similar results could be found in the benchtop NIR. The RPD of all models were over 2 which showed good performance of the models. This study indicated that the portable NIR performance was comparable with the performance of the benchtop NIR for distinction of the HOP from others, as well as for the prediction of the contents of its main fatty acids.
•The first report on the qualitative and quantitative analysis in peanut kernels by portable NIR.•The interrelationships between HOP and RP were determined by PCA.•HOP and RP were completely distinguished based on PLS-DA by portable and benchtop NIR.•Portable NIR had the same quantitative performance as benchtop NIR according PLS models.
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•Multi-block data analysis was applied for quality control in the coffee industry.•Common Dimensions showed clusters, importance of blocks and their relationships.•Metabolites related ...to cup and roasting profiles of coffee blends were identified.•Multi-block data analysis was more valuable than a principal component analysis.•Relationships between sensory characteristics and metabolites were established.
Coffee quality is determined by several factors and, in the chemometric domain, the multi-block data analysis methods are valuable to study multiple information describing the same samples. In this industrial study, the Common Dimension (ComDim) multi-block method was applied to evaluate metabolite fingerprints, near-infrared spectra, sensory properties, and quality parameters of coffee blends of different cup and roasting profiles and to search relationships between these multiple data blocks. Data fusion-based Principal Component Analysis was not effective in exploiting multiple data blocks like ComDim. However, when a multi-block was applied to explore the data sets, it was possible to demonstrate relationships between the methods and techniques investigated and the importance of each block or criterion involved in the industrial quality control of coffee. Coffee blends were distinguished based on their qualities and metabolite composition. Blends with high cup quality and lower roasting degrees were generally differentiated from those with opposite characteristics.
Near infrared (NIR) spectroscopy is a non-destructive detection technology involving NIR spectral data acquisition and chemometric treatment. The use of an NIR spectrometer is evidently crucial in ...this regard; however, traditional benchtop NIR spectrometers considerably limit usage scenarios. Accordingly, the miniaturization of spectrometers with high level performance has become a research trend. Various commercial products have been developed, and new techniques have been applied to produce more portable NIR spectrometers. This paper reviews the main types of commercial portable NIR spectrometers and summarizes as well as compares their specifications. Moreover, new techniques for promoting miniaturization and the prospects for future development are introduced.
This study built a portable NIR spectroscopy system to acquire the NIR spectra of wheat samples at disparate storage stages by means of diffuse reflectance, and the obtained NIR spectra were ...pre-processed appropriately. In order to obtain highly targeted feature wavelength variables, three variable selection methods were used to optimize the feature wavelength of the pre-processed NIR spectra. Finally, PLS quantitative detection models were developed on the basis of optimized characteristic wavelength variables to realize rapid detection of the AFB1 in wheat during storage, and the outcomes of each PLS model were compared and analyzed. The figure shows the connection between the modules and the near-infrared spectroscopy collection process of wheat samples.
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•A portable NIRS system was developed to determine the AFB1 in wheat during storage.•The characteristic wavelengths were optimized by different variable selection algorithm.•PLS models were established using selected features to determine the AFB1 in wheat.•This study provides theoretical reference for more targeted spectrometer development.
Wheat is a widely grown grain crop around the world and is highly susceptible to environmental factors during storage and transportation, resulting in the production of fungal toxins that are harmful to humans. Of these, aflatoxin B1 (AFB1) is the most prevalent and most toxic. In view of this, this study used a self-built portable near-infrared spectroscopy system to predict the AFB1 content of wheat during storage and investigated and compared the prediction effects of different wavelength selection algorithms on the constructed PLS model. Firstly, the NIR spectra of wheat samples at disparate storage stages were acquired using the NIR spectroscopy system. Secondly, the raw NIR spectra were pretreated by Savizkg-Golag (SG) smoothing, standard normal variate (SNV) and normalization in turn. Finally, three variable optimization methods, which were variable combination population analysis (VCPA), variable iterative space shrinkage approach (VISSA) and competitive adaptive reweighted sampling (CARS), were applied to select the characteristic wavelength variables of the pre-processed spectra. Partial least squares (PLS) models based on the optimized features of the three methods were established, respectively. The results obtained showed that the CARS-PLS model had the best overall effect. The root mean square error of prediction (RMSEP) for the best CARS-PLS was 2.0965 μg∙kg-1, the prediction coefficient of determination (Rp2) was 0.9935, and the ratio of prediction to deviation (RPD) was 7.3279. The CARS variable screening method was used to effectively select the characteristic wavebands associated with AFB1 in wheat, compressing the number of wavelength variables, simplifying the model structure and improving model performance. The results reveal that the self-built portable NIR spectroscopy system enables to determine the AFB1 in wheat during storage. Furthermore, through the feature optimization of spectral wavelength variables can effectively exclude undesired wavelength variable information.
Currently, one of the main demands of consumers - especially in large fruits such as watermelon - is for supermarkets to use techniques of non-invasive analysis to enable them to measure the ...sweetness of the fruits at the time of purchase, and thus avoid having to base the choice exclusively on external appearance. In addition, increasing interest is being shown by consumers in knowing the nutritional quality of healthy foods, such as watermelon. Near infrared spectroscopy (NIRS) was used to assess in situ the physicochemical and nutritional quality of half-watermelons, which is the format in which they are usually sold in supermarkets at the beginning of the season, due to their high price. A handheld, new-generation spectrophotometer was used for this purpose, and two modes of analysis, static and dynamic, were studied. The results obtained show the viability of using NIRS technology in dynamic mode at the supermarket level to obtain a reliable measurement of the sweetness of the half-fruits, thus meeting the consumers’ demand for sweet-tasting fruits. Promising results were also obtained for measuring the antioxidant activity of the half-watermelons, thus paving the way for the nutritional labelling of this healthy food at the supermarket level.
•NIRS technology to ensure consumers loyalty in future watermelon purchases.•Watermelons can label with nutritional information using NIRS technology.•NIRS inform consumers in situ of the degree of sweetness of the half-watermelons.•A portable new generation NIRS sensor was evaluated for in situ analysis.
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