Quality control of cashew apple and guava nectar by near infrared spectroscopy

The Use of Infrared Spectroscopy for the Quantification of Bioactive Compounds in Food: A Review

Infrared spectroscopy (wavelengths ranging from 750-25,000 nm) offers a rapid means of assessing the chemical composition of a wide range of sample types, both for qualitative and quantitative analyses. Its use in the food industry has increased significantly over the past five decades and it is now an accepted analytical technique for the routine analysis of certain analytes. Furthermore, it is commonly used for routine screening and quality control purposes in numerous industry settings, albeit not typically for the analysis of bioactive compounds. Using the Scopus database, a systematic search of literature of the five years between 2016 and 2020 identified 45 studies using near-infrared and 17 studies using mid-infrared spectroscopy for the quantification of bioactive compounds in food products. The most common bioactive compounds assessed were polyphenols, anthocyanins, carotenoids and ascorbic acid. Numerous factors affect the accuracy of the developed model, including the analyte class and concentration, matrix type, instrument geometry, wavelength selection and spectral processing/pre-processing methods. Additionally, only a few studies were validated on independently sourced samples. Nevertheless, the results demonstrate some promise of infrared spectroscopy for the rapid estimation of a wide range of bioactive compounds in food matrices.

Investigating Changes in pH and Soluble Solids Content of Potato during the Storage by Electronic Nose and Vis/NIR Spectroscopy

Potato is an important agricultural product, ranked as the fourth most common product in the human diet. Potato can be consumed in various forms. As customers expect safe and high-quality products, precise and rapid determination of the quality and composition of potatoes is of crucial significance. The quality of potatoes may alter during the storage period due to various phenomena. Soluble solids content (SSC) and pH are among the quality parameters experiencing alteration during the storage process. This study is thus aimed to assess the variations in SSC and pH during the storage of potatoes using an electronic nose and Vis/NIR spectroscopic techniques with the help of prediction models including partial least squares (PLS), multiple linear regression (MLR), principal component regression (PCR), support vector regression (SVR) and an artificial neural network (ANN). The variations in the SSC and pH are ascending and significant. The results also indicate that the SVR model in the electronic nose has the highest prediction accuracy for the SSC and pH (81, and 92%, respectively). The artificial neural network also managed to predict the SSC and pH at accuracies of 83 and 94%, respectively. SVR method shows the lowest accuracy in Vis/NIR spectroscopy while the PLS model exhibits the best performance in the prediction of the SSC and pH with respective precision of 89 and 93% through the median filter method. The accuracy of the ANN was 85 and 90% in the prediction of the SSC and pH, respectively.

Comparison of Proximate and Phytonutrient Compositions of Cashew Nuts and Apples from Different Geographical Areas of Burkina Faso

The cashew plant is an allogamous plant that produces two types of fruits: the nut and the cashew apple. The present study was conducted to perform a comparison of proximate and phytonutrient compositions of cashew (Anacardium occidentale L.) nuts and apples from different geographical areas of Burkina Faso. For this purpose, 60 samples of apples and kernels were collected from the three main cashew cultivation areas. The nutritional potential of cashew nuts and apples produced was evaluated to enhance their food processing. Protein, carbohydrates, lipids, dietary fibers, ascorbic acid, tannins, anthocyanins, chlorophyll, lycopene, and β-carotene contents were assessed. The results revealed high contents of lipids ( $50.42\pm 2.3$  g/100 gDW), proteins ( $22.32+-1.8$ g/100 gDW), and starch ( $12.05\pm 1.27$ g/100 g DW) in almonds. Apples, on the other hand, are rich in lipids, ascorbic acid ( $387.45\pm 17.4$ mg/100 g), soluble sugars ( $387.45\pm 17.4$ mg/100 g,), and pigments (lycopene, anthocyanin, β-carotene, and chlorophyll). In summary, almonds may be suitable as a source of lipids and related products. Apples can be used as natural antioxidants and produce juices. All of these data are important clues for cashew by-product processing. These results obtained provide a scientific basis for their food and economical valorization of cashew fruits.

Calibration of Near Infrared Spectroscopy of Apples with Different Fruit Sizes to Improve Soluble Solids Content Model Performance

The transmission spectrum of apples is affected by the fruit’s size, which leads to poor prediction performance of the soluble solids content (SSC) models built for their different apple sizes. In this paper, three sets of near infrared (NIR) spectra of apples with various apple diameters were collected by applying NIR spectroscopy detection equipment to compare the spectra differences among various apple diameter groups. The NIR spectra of apples were corrected by studying the extinction rates within different apples. The corrected spectra were used to develop a partial least squares prediction model for their soluble solids content. Compared with the prediction model of the soluble solids content of apples without size correction, the R_p of PLSR improved from 0.769 to 0.869 and RMSEP declined from 0.990 to 0.721 in the small fruit diameter group; the R_p of PLSR improved from 0.787 to 0.932 and RMSEP declined from 0.878 to 0.531 in the large fruit diameter group. The proposed apple spectra correction method is effective and can be used to reduce the influence of sample diameter on NIR spectra.

Non-destructive assessment of vitamin C in foods: a review of the main findings and limitations of vibrational spectroscopic techniques

The constant increase in the demand for safe and high-quality food has generated the need to develop efficient methods to evaluate food composition, vitamin C being one of the main quality indicators. However, its heterogeneity and susceptibility to degradation makes the analysis of vitamin C difficult by conventional techniques, but as a result of technological advances, vibrational spectroscopy techniques have been developed that are more efficient, economical, fast, and non-destructive. This review focuses on main findings on the evaluation of vitamin C in foods by using vibrational spectroscopic techniques. First, the fundamentals of ultraviolet–visible, infrared and Raman spectroscopy are detailed. Also, chemometric methods, whose use is essential for a correct processing and evaluation of the spectral information, are described. The use and importance of vibrational spectroscopy in the evaluation of vitamin C through qualitative characterization and quantitative analysis is reported. Finally, some limitations of the techniques and potential solutions are described, as well as future trends related to the utilization of vibrational spectroscopic techniques.

Nondestructive estimation of three apple fruit properties at various ripening levels with optimal Vis-NIR spectral wavelength regression data

Nondestructive estimation of fruit properties during their ripening stages ensures the best value for producers and vendors. Among common quality measurement methods, spectroscopy is popular and enables physicochemical properties to be nondestructively estimated. The current study aims to nondestructively predict tissue firmness (kgf/cm), acidity (pH level) and starch content index (%) in apples (Malus M. pumila) samples (Fuji var.) at various ripening stages using visible/near infrared (Vis-NIR) spectral data in 400–1000 nm wavelength range. Results show that non-linear regression done by an artificial neural network-cultural algorithm (ANN-CA) was able to properly estimate the investigated fruit properties. Moreover, the performance of the proposed method was evaluated for Vis-NIR data based on optimal NIR wavelength values selected by a genetic optimization tool. Regression coefficients (R) in estimated acidity, tissue firmness, and starch content properties were R=0.930±0.014, R=0.851±0.014, and R=0.974±0.006, respectively, using only the three most effective wavelengths from the acquired spectra.

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Automatic regression infrared imaging system for nondestructive estimation of physicochemical properties in apple fruit.

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Tissue firmness (kgf/cm), acidity (pH) and starch content (%) fruit properties estimated.

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Hardware used includes: spectrophotometer, photo-detector, light source, and optical fiber.

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System comprises both variable wavelength Vis-NIR ranges and fixed optimal NIR wavelength windows.

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Results: regression plots, regression (R) and determination (R²) coefficient boxplots, measured vs estimated value graphs.

Vitamins in wine: Which, what for, and how much?

Vitamins are essential compounds to yeasts, and notably in winemaking contexts. Vitamins are involved in numerous yeast metabolic pathways, including those of amino acids, fatty acids, and alcohols, which suggests their notable implication in fermentation courses, as well as in the development of aromatic compounds in wines. Although they are major components in the course of those microbial processes, their significance and impact have not been extensively studied in the context of winemaking and wine products, as most of the studies focusing on the subject in the past decades have relied on relatively insensitive and imprecise analytical methods. Therefore, this review provides an extensive overview of the current knowledge regarding the impacts of vitamins on grape must fermentations, wine-related yeast metabolisms, and requirements, as well as on the profile of wine sensory characteristics. We also highlight the methodologies and techniques developed over time to perform vitamin analysis in wines, and assess the importance of precisely defining the role played by vitamins in winemaking processes, to ensure finer control of the fermentation courses and product characteristics in a highly complex matrix.

Robust prediction performance of inner quality attributes in intact cocoa beans using near infrared spectroscopy and multivariate analysis

Fast and simultaneous determination of inner quality parameters, such as fat and moisture contents, need to be predicted in cocoa products processing. This study aimed to employ the near-infrared reflectance spectroscopy (NIRS) in predicting the quality mentioned above parameters in intact cocoa beans. Near-infrared spectral data, in a wavelength ranging from 1000 to 2500 nm, were acquired for a total of 110 bulk cocoa bean samples. Actual fat and moisture contents were measured with standard laboratory procedures using the Soxhlet and Gravimetry methods, respectively. Two regression approaches, namely principal component regression (PCR) and partial least square regression (PLSR), were used to develop the prediction models. Furthermore, four different spectra correction methods, namely multiple scatter correction (MSC), de-trending (DT), standard normal variate (SNV), and orthogonal signal correction (OSC), were employed to enhance prediction accuracy and robustness. The results showed that PLSR was better than PCR for both quality parameters prediction. Spectra corrections improved prediction accuracy and robustness, while OSC was the best correction method for fat and moisture content prediction. The maximum correlation of determination (R²) and residual predictive deviation (RPD) index for fat content were 0.86 and 3.16, while for moisture content prediction, the R² coefficient and RPD index were 0.92 and 3.43, respectively. Therefore, NIRS combined with proper spectra correction method can be used to rapidly and simultaneously predict inner quality parameters of intact cocoa beans.

NIR spectroscopy and chemometric tools to identify high content of deoxynivalenol in barley

Deoxynivalenol (DON) is one of the mycotoxins produced mainly by the Fusarium graminearum species complex in small grain cereals, including barley. This toxin can cause alimentary disorders, immune function depression and gastroenteritis. The negative health effects associated with DON coupled to the increasing concern about green and rapid methods of analysis motivated this study. In this context, near infrared (NIR) spectroscopy data were applied for exploratory analysis to distinguish barley with high and low levels of DON contamination (> or <1250 µg/kg according to the European Union threshold), by Partial Least Squares-Discriminant Analysis (PLS-DA), and to verify the performance of Partial Least Squares-Regression (PLS-R) to predict DON concentration in barley samples. Maximum values of specificity and sensitivity were achieved in the calibration set; 90.9% and 81.9% were observed in the cross-validation set for the PLS-DA classification model. PLS-R quantification of DON in barley presented low values of error (RMSEC = 101.94 µg/kg and RMSEP = 160.76 µg/kg). Thus, we found that NIR in combination with adequate chemometric tools could be applied as a green technique to monitor DON contamination in barley.

Enhanced near infrared spectral data to improve prediction accuracy in determining quality parameters of intact mango

Presented manuscript aimed to describes enhanced near infrared spectral dataset used to improve prediction performances of near infrared models in determining quality parameters of intact mango fruits. The two mentioned quality parameters are total acidity (TA) and vitamin C which corresponds to main inner attributes of fruits. Near infrared (NIR) spectra data were acquired and recorded as absorbance spectral data in wavelength range from 1000 to 2500 nm. These data were then enhanced by means of several algorithms like multiplicative scatter correction (MSC), baseline linear correction (BLC) and combination of them (MSC+BLC). Prediction models, used to determine TA and vitamin C were established using most common approach: partial least square regression (PLS) based on raw and enhanced spectral data respectively. Prediction performances can be evaluated based on prediction accuracy and robustness, by looking statistical indicators presented as coefficient of determination (R²) and correlation (r), root mean square error (RMSE) and residual predictive deviation (RPD). Enhanced NIR spectral dataset can be employed as a rapid, effective and non-destructive method to determine inner quality parameters of intact fruits.

Performance Evaluation of Two Commercially Available Portable Spectrometers to Non-Invasively Determine Table Grape and Peach Quality Attributes

Near-infrared (NIR) spectroscopy has been used to non-destructively and rapidly evaluate the quality of fresh agricultural produce. In this study, two commercially available portable spectrometers (F-750: Felix Instruments, WA, USA; and SCiO: Consumer Physics, Tel Aviv, Israel) were evaluated in the wavelength range between 740 and 1070 nm to non-invasively predict quality attributes, including the dry matter (DM), and total soluble solids (TSS) content of three fresh table grape cultivars (‘Autumn Royal’, ‘Timpson’, and ‘Sweet Scarlet’) and one peach cultivar (‘Cassie’). Prediction models were developed using partial least-square regression (PLSR) to correlate the NIR absorbance spectra with the invasive quality measurements. In regard to grapes, the best DM prediction models yielded an R2 of 0.83 and 0.81, a ratio of standard error of performance to standard deviation (RPD) of 2.35 and 2.29, and a root mean square error of prediction (RMSEP) of 1.40 and 1.44; and the best TSS prediction models generated an R2 of 0.97 and 0.95, an RPD of 5.95 and 4.48, and an RMSEP of 0.53 and 0.70 for the F-750 and SCiO spectrometers, respectively. Overall, PLSR prediction models using both spectrometers were promising to predict table grape quality attributes. Regarding peach, the PLSR prediction models did not perform as well as in grapes, as DM prediction models resulted in an R2 of 0.81 and 0.67, an RPD of 2.24 and 1.74, and an RMSEP of 1.28 and 1.66; and TSS resulted in an R2 of 0.62 and 0.55, an RPD of 1.55 and 1.48, and an RMSEP of 1.19 and 1.25 for the F-750 and SCiO spectrometers, respectively. Overall, the F-750 spectrometer prediction models performed better than those generated by using the SCiO spectrometer data.

Control of ascorbic acid in fortified powdered soft drinks using near-infrared spectroscopy (NIRS) and multivariate analysis

Powdered soft drinks (PSDs), fortified with antioxidants such as ascorbic acid (AA), are normally controlled by titration or chromatographic methods. This study evaluated the feasibility of using near-infrared spectroscopy (NIRS) and multivariate analysis to predict AA contents in PSDs as an alternative not-destructive method. The AA content of sixty-seven samples of commercial fortified grape and passion fruit PSDs was analyzed by the standard method (titration) and showed significant variance between flavors within the same brand. In addition, 75% of the samples required from 0.3 to 10.2 more cups of grape than passion fruit flavor to supply the AA Reference Nutrient Intake for children and adults. Spectral and reference data sets were split into calibration and validation sets. Partial least squares regression models were built and validated for the determination of AA in both PSDs. The model's basic statistics for grape flavor PSDs (RMSEC = 0.49 mg g^-1, R_cal ² = 0.84; RMSECV = 0.67 mg g^-1, R_CV ² = 0.70; RMSEP = 0.50 mg g^-1, R_pred ² = 0.84), and that for passion fruit flavor PSDs (RMSEC = 0.24 mg g^-1, R_cal ² = 0.95; RMSECV = 0.56 mg g^-1, R_CV ² = 0.76; RMSEP de 0.57 mg g^-1, R_pred ² = 0.72) indicated that NIRS-PLS methodology produced reasonable results. The limits of detection and quantification obtained showed that the method is useful to detect and quantify AA in the studied samples. A new set of grape drinks was used for external prediction and the RMSEP was 0.62 mg g^-1, R_pred ² was 0.72. Based on the results, NIRS-multivariate analysis proved to be useful for quality control of AA in commercialized grape and passion fruit in PSDs and a faster, objective and environmentally friendly method alternative to standard methods.

Metabolomic Variability of Different Genotypes of Cashew by LC-Ms and Correlation with Near-Infrared Spectroscopy as a Tool for Fast Phenotyping

The objective of the present work was to develop an advanced fast phenotyping tool to explore the cashew apple compositions from different genotypes, based on a portable near-infrared (MicroNIR) spectroscopy. This will be in addition to associating the variability of the respective cashew apple pulps with the genotypes by ultra-performance liquid chromatography (UPLC), coupled with high-resolution mass spectrometry (HRMS). The NIR analysis is a non-destructive, low-cost procedure that provides prompt results, while considering the morphology of different cashew apples (shape, size, and color). The UPLC-HRMS analysis is characterized by specific bioactive compounds, such as the derivatives of hydroxybutanoic acid, galloyl, and flavonoids. Furthermore, both techniques allowed the identification of a group of accessions, which presented similarities among the chemical profiling. However, to improve the understanding of cashew chemical and physical variability, further variables related to the cashew apple composition, such as edaphoclimatic conditions, should be considered for future studies. These approaches lead to the conclusion that these two tools are useful for the maintenance of BAG-Caju (Cashew Germplasm Bank) and for the cashew-breeding program.

A review on the application of vibrational spectroscopy to the chemistry of nuts

Nuts are highly appreciated due to their nutritional relevance and flavour, being the source of many desirable and healthy compounds as polyunsaturated fatty acids and antioxidants. Their characterization became the target of many studies in the last years through conventional analytical techniques as chromatographic ones. Due to the limitations associated to these techniques, as time, cost and environmental concerns, spectroscopic techniques have been increasingly pointed as reliable alternatives. Either applied to raw materials quality control or to more complex process, as industrial in-line monitoring, spectroscopic techniques, namely vibrational spectroscopy, are gathering strong acceptance. This paper presents a review on the application of vibrational spectroscopy, infrared and Raman, to nuts characterization. Estimates of several qualitative and quantitative parameters, origin authentication and/or adulteration in almonds, peanuts, pine nuts, hazelnuts, walnuts, Brazil nuts, cashews, chestnuts and pistachios will be covered. Advantages and limitations of these techniques and future trends will also be discussed.