Measurement errors and implications for preprocessing in miniaturised near-infrared spectrometers: Classification of sweet and bitter almonds as a case of study.

Near-infrared (NIR) spectroscopy is a well-established analytical technique that has been used in many applications over the years. Due to the advancements in the semiconductor industry, NIR instruments have evolved from benchtop instruments to miniaturised portable devices. The miniaturised NIR instruments have gained more interest in recent years because of the fast and robust measurements they provide with almost no sample pretreatments. However, due to the very different configurations and characteristics of these instruments, they need a dedicated optimization of the measurement conditions, which is crucial for obtaining reliable results. To comprehensively grasp the capabilities and potentials offered by these sensors, it is imperative to examine errors that can affect the raw data, which is a facet frequently overlooked. In this study, measurement error covariance and correlation matrices were calculated and then visually inspected to gain insight into the error structures associated with the devices, and to find the optimal preprocessing technique that may result in the improvement of the models built. This strategy was applied to the classification of sweet and bitter almonds, which were measured with the three portable low-cost NIR devices (SCiO, FlameNIR+ and NeoSpectra Micro Development Kit) after removing the shelled, since their classification is of utmost importance for the almond industry. The results showed that bitter almonds can be classified from sweet almonds using any of the instruments after selecting the optimal preprocessing, obtained through inspection of covariance and correlation matrices. Measurements obtained with FlameNIR + device provided the best classification models with an accuracy of 98 %. The chosen strategy provides new insight into the performance characterization of the fast-growing miniaturised NIR instruments.

Exploring New Horizons for Wine Grapes: Modulating Functional Effects by Varying Harvest Timing and Solar Exposure.

Grenache (GN) and Cabernet Sauvignon (CS) are two traditional red grape varieties widely cultivated in the Mediterranean area and both late-ripening cultivars, which makes them less sensitive to global warming conditions and more stable to harvest timing. Although different studies have evaluated the final antioxidant properties of grapes and pomaces, few studies have explored the effect of sun exposure and harvest on the nutritional and antioxidant properties of these products. This study investigates the control of sunlight and ripening as tools to tailor nutritional and antioxidant properties of grape juices (GJ) and their byproducts (pomace GP). The compositional analysis showed no significant (p ≥ 0.05) differences associated to either harvesting timing or exposure to sunlight for either of the two studied varieties. However, differences (p ≤ 0.05) were observed between varieties of protein and total dietary fibre (TDF). CS protein content ranged from 0.52 to 3.88 (g 100 g-1) in GJ and from 1.0 to 1.32 (g 100 g-1) in GP; meanwhile, GN had higher protein values in GJ (from 2.11 to 4.77 g 100 g-1) and GP (from 5.11 to 6.75 g 100 g-1). The opposite behaviour was observed in TDF; CS grape had higher values for juice (from 11.43 to 19.53 g 100 g-1) and pomace (from 42.20 to 65.80 g 100 g-1) than GN (from 11.43 to 17.22 g 100 g-1 in juice and from 25.90 to 54.0 g 100 g-1 in pomace). The total phenolic content (TP) in GP was 100 times higher than in the juices and showed a much less pronounced evolution compared to the GJ during the harvesting time. GN TP values ranged from 5835 to 8772 mg GAE 100 g-1; meanwhile, CS values ranged from 7637 to 9040 mg GAE 100 g-1. A significant (p ≤ 0.05) correlation between the TP total antioxidant capacity (TAC) results was observed, regardless of variety, harvesting time, and sunlight exposure. These findings show how the control of different factors can contribute to obtain modified grape-derived products from conventional varieties beyond the wine market.

Analysing olive ripening with digital image RGB histograms.

Digital images are commonly used to monitor processes that are based on colour changes due to their simplicity and easy capture. Colour information in these images can be analysed objectively and accurately using colour histograms. One such process is olive ripening, which is characterized by changes in chemical composition, sensory properties and can be followed by changes in physical appearance, mainly colour. The reference method to quantify the ripeness of olives is the Maturity Index (MI), which is determined by trained experts assigning individual olives into a colour scale through visual inspection. Instead, this study proposes a methodology based on Chemometrics Assisted Colour Histogram-based Analytical Systems (CACHAS) to automatically assess the MI of olives based on R, G, and B colour histograms derived from digital images. The methodology was shown to be easily transferable for routine analysis and capable of controlling the ripening of olives. The study also confirms the high potential of digital images to understand the ripening process of olives (and potentially other fruits) and to predict the MI with satisfactory accuracy, providing an objective and reproducible alternative to visual inspection of trained experts.

Assessment of Variability Sources in Grape Ripening Parameters by Using FTIR and Multivariate Modelling.

The variability in grape ripening is associated with the fact that each grape berry undergoes its own biochemical processes. Traditional viticulture manages this by averaging the physicochemical values of hundreds of grapes to make decisions. However, to obtain accurate results it is necessary to evaluate the different sources of variability, so exhaustive sampling is essential. In this article, the factors "grape maturity over time" and "position of the grape" (both in the grapevine and in the bunch/cluster) were considered and studied by analyzing the grapes with a portable ATR-FTIR instrument and evaluating the spectra obtained with ANOVA-simultaneous component analysis (ASCA). Ripeness over time was the main factor affecting the characteristics of the grapes. Position in the vine and in the bunch (in that order) were also significantly important, and their effect on the grapes evolves over time. In addition, it was also possible to predict basic oenological parameters (TSS and pH with errors of 0.3 °Brix and 0.7, respectively). Finally, a quality control chart was built based on the spectra obtained in the optimal state of ripening, which could be used to decide which grapes are suitable for harvest.

Quantitation of endogenous amount of ethanol, methanol and acetaldehyde in ripe fruits of different Spanish olive varieties.

BACKGROUND: The use of healthy olives and their good management along the production process are necessary to obtain the best quality virgin olive oils. One parameter related to the health of the olives is the content of fatty acid alkyl esters. Because these come from the esterification of C16 and C18 free fatty acids with short chain alcohols, the control of methanol, ethanol and acetaldehyde (precursor of ethanol) and their origin (endogenous or from fermentation) is essential. The present study reports the endogenous amount of these compounds in some of the main Spanish olive varieties. For their analyses, headspace solid phase micro-extraction was applied and, to ensure quantitation reliability, the matrix-matched technique was used to build the calibration lines. RESULTS: For healthy and mature olives, the contents of ethanol and methanol are much higher and vary within a wider range than those corresponding to acetaldehyde. Because olives were not directly analyzed but previously homogenized, there was no correlation between the olive size parameters and the contents of the compounds investigated. However, these contents are characteristic of each variety. When comparing healthy and unhealthy olives, significant differences were only observed for ethanol contents. CONCLUSION: Higher contents of short alcohols are not only the result of an unhealthy or poor state of the fruits, but also the variety. Therefore, because these alcohols are precursors of fatty acid alkyl esters, the maximum permissible content of the latter should not be set at a single value for all olive varieties. © 2020 Society of Chemical Industry.

Authentication of whisky due to its botanical origin and way of production by instrumental analysis and multivariate classification methods.

Headspace mass-spectrometry (HS-MS), mid infrared (MIR) and UV-vis spectroscopy were used to authenticate whisky samples from different origins and ways of production ((Irish, Spanish, Bourbon, Tennessee Whisky and Scotch). The collected spectra were processed with partial least-squares discriminant analysis (PLS-DA) to build the classification models. In all cases the five groups of whiskies were distinguished, but the best results were obtained by HS-MS, which indicates that the biggest differences between different types of whisky are due to their aroma. Differences were also found inside groups, showing that not only raw material is important to discriminate samples but also the way of their production. The methodology is quick, easy and does not require sample preparation.

Prediction of olive oil sensory descriptors using instrumental data fusion and partial least squares (PLS) regression.

Headspace-Mass Spectrometry (HS-MS), Fourier Transform Mid-Infrared spectroscopy (FT-MIR) and UV-Visible spectrophotometry (UV-vis) instrumental responses have been combined to predict virgin olive oil sensory descriptors. 343 olive oil samples analyzed during four consecutive harvests (2010-2014) were used to build multivariate calibration models using partial least squares (PLS) regression. The reference values of the sensory attributes were provided by expert assessors from an official taste panel. The instrumental data were modeled individually and also using data fusion approaches. The use of fused data with both low- and mid-level of abstraction improved PLS predictions for all the olive oil descriptors. The best PLS models were obtained for two positive attributes (fruity and bitter) and two defective descriptors (fusty and musty), all of them using data fusion of MS and MIR spectral fingerprints. Although good predictions were not obtained for some sensory descriptors, the results are encouraging, specially considering that the legal categorization of virgin olive oils only requires the determination of fruity and defective descriptors.

Olive oil sensory defects classification with data fusion of instrumental techniques and multivariate analysis (PLS-DA).

Three instrumental techniques, headspace-mass spectrometry (HS-MS), mid-infrared spectroscopy (MIR) and UV-visible spectrophotometry (UV-vis), have been combined to classify virgin olive oil samples based on the presence or absence of sensory defects. The reference sensory values were provided by an official taste panel. Different data fusion strategies were studied to improve the discrimination capability compared to using each instrumental technique individually. A general model was applied to discriminate high-quality non-defective olive oils (extra-virgin) and the lowest-quality olive oils considered non-edible (lampante). A specific identification of key off-flavours, such as musty, winey, fusty and rancid, was also studied. The data fusion of the three techniques improved the classification results in most of the cases. Low-level data fusion was the best strategy to discriminate musty, winey and fusty defects, using HS-MS, MIR and UV-vis, and the rancid defect using only HS-MS and MIR. The mid-level data fusion approach using partial least squares-discriminant analysis (PLS-DA) scores was found to be the best strategy for defective vs non-defective and edible vs non-edible oil discrimination. However, the data fusion did not sufficiently improve the results obtained by a single technique (HS-MS) to classify non-defective classes. These results indicate that instrumental data fusion can be useful for the identification of sensory defects in virgin olive oils.

Data fusion methodologies for food and beverage authentication and quality assessment - a review.

The ever increasing interest of consumers for safety, authenticity and quality of food commodities has driven the attention towards the analytical techniques used for analyzing these commodities. In recent years, rapid and reliable sensor, spectroscopic and chromatographic techniques have emerged that, together with multivariate and multiway chemometrics, have improved the whole control process by reducing the time of analysis and providing more informative results. In this progression of more and better information, the combination (fusion) of outputs of different instrumental techniques has emerged as a means for increasing the reliability of classification or prediction of foodstuff specifications as compared to using a single analytical technique. Although promising results have been obtained in food and beverage authentication and quality assessment, the combination of data from several techniques is not straightforward and represents an important challenge for chemometricians. This review provides a general overview of data fusion strategies that have been used in the field of food and beverage authentication and quality assessment.

Identification of olive oil sensory defects by multivariate analysis of mid infrared spectra.

Mid-infrared (MIR) spectra (4000-600 cm(-1)) of olive oils were analyzed using chemometric methods to identify the four main sensorial defects, musty, winey, fusty and rancid, previously evaluated by an expert sensory panel. Classification models were developed using partial least squares discriminant analysis (PLS-DA) to distinguish between extra-virgin olive oils (defect absent) and lower quality olive oils (defect present). The most important spectral ranges responsible for the discrimination were identified. PLS-DA models were able to discriminate between defective and high quality oils with predictive abilities around 87% for the musty defect and around 77% for winey, fusty and rancid defects. This methodology advances instrumental determination of results previously only achievable with a human test panel.

Thermal oxidation process accelerates degradation of the olive oil mixed with sunflower oil and enables its discrimination using synchronous fluorescence spectroscopy and chemometric analysis.

We have investigated the effect of thermal treatment on the discrimination of pure extra virgin olive oil (EVOO) samples from EVOO samples adulterated with sunflower oil. Two groups of samples were used. One group was analyzed at room temperature (25°C) and the other group was thermally treated in a thermostatic water bath at 75°C for 8h, in contact with air and with light exposure, to favor oxidation. All samples were then measured with synchronous fluorescence spectroscopy. Fluorescence spectra were acquired by varying the excitation wavelength in the region from 250 to 720nm. In order to optimize the differences between excitation and emission wavelengths, four constant differential wavelengths, i.e., 20nm, 40nm, 60nm and 80nm, were tried. Partial least-squares discriminant analysis (PLS-DA) was used to discriminate between pure and adulterated oils. It was found that the 20nm difference was the optimal, at which the discrimination models showed the best results. The best PLS-DA models were those built with the difference spectra (75-25°C), which were able to discriminate pure from adulterated oils at a 2% level of adulteration. Furthermore, PLS regression models were built to quantify the level of adulteration. Again, the best model was the one built with the difference spectra, with a prediction error of 1.75% of adulteration.

Fast and robust discrimination of almonds (Prunus amygdalus) with respect to their bitterness by using near infrared and partial least squares-discriminant analysis.

In this study, near-infrared spectroscopy (NIR) coupled to chemometrics is used to develop a fast, simple, non-destructive and robust method for discriminating sweet and bitter almonds (Prunus amygdalus) by the in situ measurement of the kernel surface without any sample pre-treatment. Principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA) models were built to discriminate both types of almonds, obtaining high levels of sensitivity and specificity for both classes, with more than 95% of the samples correctly classified and discriminated. Moreover, the almonds were also analysed by Raman spectroscopy, the reference technique for this type of analysis, to validate and confirm the results obtained by NIR.

Quantification of phenolic compounds during red winemaking using FT-MIR spectroscopy and PLS-regression.

We present a rapid method to quantify phenolic compounds all during the red winemaking process using Fourier transform mid-infrared (FT-MIR) spectroscopy and chemometrics. To get the reference values, we used the usual UV­vis spectroscopy methods, and the compounds studied were evaluated as total phenolic compounds (TPC), total anthocyanins (TA), and condensed tannins (CT). Sampling from five different grape varieties (Merlot, Tempranillo, Syrah, Cariñena, and Cabernet sauvignon), harvested at different ripening states, and monitored over 10 days of vinification produced a total of 600 spectra. These were used to build and validate four different predictive models by partial least-squares (PLS) regression. The spectral regions selected for each model were between 979 and 2989 cm(­1), and when selecting the most suitable one in each case, good values of performance parameters were obtained (R2(val) > 0.95 and RPD > 4.0 for TPC; R2(val) > 0.90 and RPD > 3.0 for TA; R2(val) < 0.8 and RPD < 3.0 for CT). Furthermore, also more specific PLS regression models for each phenolic parameter and each grape variety were developed using different regions with results similar to those obtained when dealing with all of the grape varieties. It is concluded that FT-MIR spectroscopy together with multivariate calibration could be a rapid and valuable tool for wineries to carry out the monitoring of phenolic compound extraction during winemaking.

Chemical characterization of commercial Sherry vinegar aroma by headspace solid-phase microextraction and gas chromatography-olfactometry.

The sensorial representativeness of the headspace solid-phase microextraction (HS-SPME) aroma extract from commercial Sherry vinegars has been determined by direct gas chromatography-olfactometry (D-GCO). Extracts obtained under optimal conditions were used to characterize the aroma of these vinegars by means of GCO and aroma extract dilution analysis (AEDA). Among the 37 different odorants determined, 13 of them were identified for the first time in Sherry vinegars: 2 pyrazines (3-isopropyl-2-methoxypyrazine, 3-isobutyl-2-methoxypyrazine), 2 sulfur compounds (methanethiol, dimethyl trisulfide), 1 unsaturated ketone (1-octen-3-one), 1 norisoprenoid (ß-damascenone), 1 ester (ethyl trans-cinnamate) and 6 aldehydes (2- and 3-methylbutanal, octanal, nonanal, (E)-2-nonenal and (E,E)-2,4-decadienal). The determination of the odor thresholds in a hydroacetic solution together with the quantitative analysis-which was also performed using the simple and fast SPME technique-allowed obtaining the odor activity values (OAV) of the aromatic compounds found. Thus, a first pattern of their sensory importance on commercial Sherry vinegar aroma was provided.

Determination of roasted pistachio (Pistacia vera L.) key odorants by headspace solid-phase microextraction and gas chromatography-olfactometry.

Key odorants in roasted pistachio nuts have been determined for the first time. Two different pistachio varieties (Fandooghi and Kerman) have been analyzed by means of headspace solid-phase microextraction (HS-SPME) and gas chromatography-olfactometry (GCO). The aroma extract dilution analyses (AEDA) applied have revealed 46 and 41 odor-active regions with a flavor dilution (FD) factor≥64 for the Fandooghi and the Kerman varieties, respectively, and 39 of them were related to precisely identified compounds. These included esters, pyrazines, aldehydes, acids, furans, and phenols. The results show that the Fandooghi variety presents, not only more odor-active regions but also higher FD factors than the Kerman variety that can lead to the conclusion that the first variety has a richer aromatic profile than the second one. The descriptive sensory analysis (DSA) showed that the roasted, chocolate/coffee, and nutty attributes were rated significantly higher in the Fandooghi variety, whereas the green attribute was significantly higher in the Kerman one.

Application of FT-MIR spectroscopy for fast control of red grape phenolic ripening.

The content of phenolic compounds determines the state of phenolic ripening of red grapes and is a key criterion in setting the harvest date to produce quality red wines. In this study, the feasibility of Fourier transform mid-infrared (FT-MIR) spectroscopy combined with partial least-squares (PLS) regression to quantify phenolic compounds is reported. The reference methods used for quantifying these compounds (which were evaluated as total phenolic compounds, total anthocyanins, and condensed tannins) were the usual ones used in cellars that employed UV-vis spectroscopy. To take into account the high natural variability of grapes when building the calibration models, fresh grapes from six varieties, at different phenolic ripening states were harvested during three vintages. Destemmed and crushed grapes were subjected to an accelerated extraction process and used as calibration standards. A total of 192 extracts (objects) were obtained, and these were divided into a training set (106 objects) and a test set (86 objects) to evaluate the predictive ability of the models. Among the different MIR regions of the extract raw spectra, those that provided the highest variability on the absorption were selected. The results showed that the best PLS regression model was the one obtained when working in the region of 1168-1457 cm(-1) because it gave the most accurate and robust prediction for total phenolic compounds (RMSEP%=4.3 and RPD=4.5), total anthocyanins (RMSEP%=5.9 and RPD=3.5), and condensed tannins (RMSEP%=5.8 and RPD=3.8). Therefore, it can be concluded that FT-MIR spectroscopy can be a fast and reliable technique for monitoring the phenolic ripening in red grapes during the harvest period.

Comparative study of two extraction techniques to obtain representative aroma extracts for being analysed by gas chromatography-olfactometry: application to roasted pistachio aroma.

This research paper presents a comparative study of two different extraction and concentration techniques to obtain representative pistachio aroma extracts: the traditional direct solvent extraction (DSE) followed by high-vacuum transfer (HVT) and the headspace solid-phase microextraction (HS-SPME). The results showed that, although both techniques provide accurate information about the aromatic composition that will be perceived by the consumer, the precision in terms of within-day repeatability and between-days repeatability (intermediate precision) of the chromatographic areas presented better values for HS-SPME than for DSE-HVT. Moreover the solvent-free HS-SPME allows the extraction of more odour-active regions, requires very little sample handling and shorter time for sampling.

Comparison of three extraction methods used to evaluate phenolic ripening in red grapes.

Because there is not a single method for carrying out phenolic ripening analysis, it is very difficult to compare the results obtained by different researchers. In this study, the three most widely used extraction methods of polyphenols (Glories, AWRI, and ITV) have been analytically compared by evaluating two of the most important parameters for the wine industry: total polyphenols and total anthocyanins. Samples from different grape varieties (Tempranillo, Garnacha, Carinena, Syrah, Merlot, and Cabernet Sauvignon), from three different vintages (2006, 2007, and 2008), and at different ripening states (from the beginning of ripeness until harvest) were analyzed to obtain a wide range of representative phenolic contents. To avoid external interferences on the comparisons, the same grape puree was used to make the maceration assays using the different solvents according to each extraction method. Although every extraction method exhibits a different extraction efficiency, the correlation between the results obtained with each one was very good both for total anthocyanins and for total polyphenols. Thus, after having determined a parameter value of the phenolic ripeness using a specific method, the relationship found can be used to predict the parameter value of the phenolic ripeness provided by the other two methods.

Determination of ageing time of spirits in oak barrels using a headspace-mass spectrometry (HS-MS) electronic nose system and multivariate calibration.

The aromatic composition of sugar cane spirits and, in general, of alcoholic beverages, is mainly influenced by the ageing process in wood barrels. There are several factors that affect the quality of the final aged product, but the time of the storage in the barrel is perhaps the most important one. Ageing time must therefore be controlled in order to detect counterfeits; however, this parameter is very difficult to control and, at present, there is no analytical method available to determine it. We propose a quantitative method for determining the ageing time of sugar cane spirits in oak barrels by using an electronic nose based on coupling directly a headspace sampler to a mass spectrometer (HS-MS), and multivariate calibration. The method developed is simple and provides, in 5 min, the ageing time of spirits with an accuracy of about 1 month.

Application of a headspace mass spectrometry system to the differentiation and classification of wines according to their origin, variety and ageing.

The system based on coupling a headspace sampler to a mass spectrometer (HS-MS), considered a kind of electronic nose (e-nose), is an emerging technique in the field of food aroma analysis. The global mass spectrum this system provides is a fingerprint of each sample analysed that contains the information related to volatile composition of the sample. The use of chemometric techniques allows to compare the spectra of the samples and then, to classify them according to different properties. In this paper, we present the development of a method for wine analysis using a HS-MS system and multivariate analysis techniques. The method was successfully applied to differentiate and classify wines according to its origin, variety and ageing. The main advantages of the proposed methodology are the minimum sample preparation required and the speed of analysis (10 min/sample).