Exploring the relevance of the type of horizontal separator to optimize the extraction efficacy for the Arbequina variety.

The productivity of virgin olive oil depends not only on agronomic factors but also on the technological factors of the extraction process. The 'Arbequina' variety has extractability problems, which is a challenge for master millers anywhere. This work aims to evaluate the behavior of different decanters and seeks to modulate the effect of some processing parameters and their interactions with oil extraction efficiency in the case of 'Arbequina.' Fruit characteristics, processing parameters, and extractability were collected over 10 years from 38 decanters that belong to five different brands. The results have shown that fruit moisture is the most relevant factor for oil extractability, especially over 52%. Furthermore, extractability is positively correlated with malaxing temperature, addition of water, and total fat content in the fruit. However, the results show that before applying a regulation, the type of decanter must be considered. The model used in this study has allowed us to optimize the regulations for each type of decanter to reduce oil losses within the pomace, achieving an extraction efficiency within the range of 78%-91.5%. In fact, the best extraction efficiency results (91.5%) were obtained by processing at temperatures >26°C and water injection of 5%.

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.

Effect of Hopper Loading on the Formation of Alkyl Alcohols in Olive Fruits and Its Relationship with Sensory Quality Losses of Virgin Olive Oil.

The storage of olives in large hoppers is a widespread practice in oil mills, but these large volumes and their unloading can cause a physical deterioration of the olives that will affect the quality of the oil obtained. This research deals with the effect of hopper charge on the formation of alkyl alcohols in olive fruits and its relationship with the sensory quality losses of 'Arbequina' virgin olive oil. The contents of ethanol, methanol, and acetaldehyde were measured in olive samples loaded and stored for a short time in a large hopper and analyzed at three different hopper-discharging times, which are related to three different positions inside the hopper. The corresponding oil from each sampling was obtained by using ABENCOR and was evaluated by a trained tasting panel. Results showed that the ethanol content in olives increased during their storage in the hopper, while methanol and acetaldehyde contents did not show significant differences. Regarding their position in the hopper, fruits located at the bottom or on the lateral sides showed a greater deterioration. The sensory analyses showed an inverse relationship between the positive attributes of olive oils and their content of alcohols.

Monitoring the Evolution of the Aroma Profile of Lager Beer in Aluminium Cans and Glass Bottles during the Natural Ageing Process by Means of HS-SPME/GC-MS and Multivariate Analysis.

Headspace solid-phase microextraction coupled to gas chromatography-mass spectrometry (HS-SPME/GC-MS), sensory evaluation, and multivariate analysis were applied to monitor and compare the evolution of the aromatic profile of a lager beer in different types of containers (aluminum cans and glass bottles) during the natural ageing process. Samples were aged naturally for a year in the absence of light with a controlled temperature of around 14 °C +/- 0.5 °C. The sensory evaluation applied was a blind olfactometric triangle test between canned and bottled samples at different periods of aging: fresh, 6 months, and 11 months. The sensory evaluation showed that the panelists were able to differentiate between samples, except for the fresh samples from the brewery. A total of 34 volatile compounds were identified using the HS-SPME/GC-MS technique for both packaging types in this experiment. The application of multivariate analysis to the GC-MS data showed that the samples could not be differentiated according to the type of packaging but could be differentiated by the ageing time. The results showed that the combination of sensory, HS-SPME-GC-MS, and multivariate analysis seemed to be a valuable tool for monitoring and identifying possible changes in the aroma profile of a beer during its shelf life. Furthermore, the results showed that storing beer under optimal conditions helped preserve its quality during its shelf life, regardless of the type of packaging (aluminum can and glass bottle).

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.

Multivariate Analysis of the Influence of Microfiltration and Pasteurisation on the Quality of Beer during Its Shelf Life.

Gas chromatography-mass spectrometry (GC-MS), physicochemical and microbiological analyses, sensory descriptive evaluation, and multivariate analyses were applied to evaluate the efficiencies of microfiltration and pasteurization processes during the shelf life of beer. Samples of microfiltered and pasteurised beer were divided into fresh and aged groups. A forced ageing process, which consisted of storing fresh samples at 55° C for 6 days in an incubator and then keeping them under ambient conditions prior to analysis, was applied. Physicochemical analysis showed that both microfiltered and pasteurised samples had a slight variation in apparent extract, pH, and bitterness. The samples that underwent heat treatment had lower colour values compared with those that were microfiltered. Chromatographic peak areas of vicinal diketones increased in both fresh and aged samples. The results of the microbiological analysis revealed spoilage lactic acid bacteria (Lactobacillus) and yeasts (Saccharomyces and non-Saccharomyces) in fresh microfiltered samples. In the GC-MS analysis, furfural, considered by many authors as a heat indicator, was detected only in samples that underwent forced ageing and not in samples that were subjected to thermal pasteurisation. Finally, sensory analysis found differences in the organoleptic properties of fresh microfiltered samples compared with the rest of the samples.

Exploring the Analytical Complexities in Insect Powder Analysis Using Miniaturized NIR Spectroscopy.

Insects have been a food source for humans for millennia, and they are actively consumed in various parts of the world. This paper aims to ascertain the feasibility of portable near-infrared (NIR) spectroscopy as a reliable and fast candidate for the classification of insect powder samples and the prediction of their major components. Commercially-available insect powder samples were analyzed using two miniaturized NIR instruments. The samples were analyzed as they are and after grinding, to study the effect of the granulometry on the spectroscopic analyses. A homemade sample holder was designed and optimized for making reliable spectroscopic measurements. Classification was then performed using three classification strategies, and partial least squares (PLS) regression was used to predict the macronutrients. The results obtained confirmed that both spectroscopic sensors were able to classify insect powder samples and predict macronutrients with an adequate detection limit.

An Overview of the Application of Multivariate Analysis to the Evaluation of Beer Sensory Quality and Shelf-Life Stability.

Achieving beer quality and stability remains the main challenge for the brewing industry. Despite all the technologies available, to obtain a high-quality product, it is important to know and control every step of the beer production process. Since the process has an impact on the quality and stability of the final product, it is important to create mechanisms that help manage and monitor the beer production and aging processes. Multivariate statistical techniques (chemometrics) can be a very useful tool for this purpose, as they facilitate the extraction and interpretation of information from brewing datasets by managing the connections between different types of data with multiple variables. In addition, chemometrics could help to better understand the process and the quality of the product during its shelf life. This review discusses the basis of beer quality and stability and focuses on how chemometrics can be used to monitor and manage the beer quality parameters during the beer production and aging processes.

Unravelling error sources in miniaturized NIR spectroscopic measurements: The case study of forages.

The use of miniaturized NIR spectrometers is spreading over the scientific literature with a particular focus on developing methods as rapid and easy-to-use as possible and following the philosophy of green analytical chemistry. Several applications and studies are typically presented by comparing results obtained with benchtop instrumentation even when the analytical strategies are substantially different. Indeed, analytical applications that include the use of miniaturized instrumentation are subject to several sources of variability that need to be known at the time of method development. In this study, different statistical strategies were employed to understand the features and limitations of handheld NIR instruments. Because of the high interest in real applications, a common type of hygroscopic powder sample was selected: forages. A step-by-step methodology is presented to statistically address the different issues to consider in order to obtain realistic models when using miniaturized NIR spectrometers. We demonstrate how a careful evaluation of the sources of variability related to an experiment can help in the understanding of the system under study in order to obtain a more reliable development of the method and consciously choose the analytical parameters and strategies of analysis. The results were also compared with those achieved on the same dataset from a benchtop system in order to provide references analogous with those in the literature.

Measurement Strategies for the Classification of Edible Oils Using Low-Cost Miniaturised Portable NIR Instruments.

Miniaturised near-infrared (NIR) instruments have been increasingly used in the last few years, and they have become useful tools for many applications on different types of samples. The market already offers a wide variety of these instruments, each one having specific requirements for the correct acquisition of the instrumental signal. This paper presents the development and optimisation of different measuring strategies for two miniaturised NIR instruments in order to find the best measuring conditions for the rapid and low-cost analysis of olive oils. The developed strategies have been applied to the classification of different samples of olive oils, obtaining good results in all cases.

Application of Spectrometric Technologies in the Monitoring and Control of Foods and Beverages.

In order to obtain high-quality products and gain a competitive advantage, food producers seek improved manufacturing processes, particularly when physicochemical and sensory properties add significant value to the product [...].

Robust Fourier transformed infrared spectroscopy coupled with multivariate methods for detection and quantification of urea adulteration in fresh milk samples.

Urea is added as an adulterant to give milk whiteness and increase its consistency for improving the solid not fat percentage, but the excessive amount of urea in milk causes overburden and kidney damages. Here, an innovative sensitive methodology based on near-infrared spectroscopy coupled with multivariate analysis has been proposed for the robust detection and quantification of urea adulteration in fresh milk samples. In this study, 162 fresh milk samples were used, those consisting 20 nonadulterated samples (without urea) and 142 with urea adulterant. Eight different percentage levels of urea adulterant, that is, 0.10%, 0.30%, 0.50%, 0.70%, 0.90%, 1.10%, 1.30%, and 1.70%, were prepared, each of them prepared in triplicates. A Frontier NIR spectrophotometer (BSEN60825-1:2007) by Perkin Elmer was used for scanning the absorption of each sample in the wavenumber range of 10,000-4,000 cm-1, using 0.2 mm path length CaF2 sealed cell at resolution of 2 cm-1. Principal components analysis (PCA), partial least-squares discriminant analysis (PLS-DA), and partial least-squares regressions (PLSR) methods were applied for the multivariate analysis of the NIR spectral data collected. PCA was used to reduce the dimensionality of the spectral data and to explore the similarities and differences among the fresh milk samples and the adulterated ones. PLS-DA also showed the discrimination between the nonadulterated and adulterated milk samples. The R-square and root mean square error (RMSE) values obtained for the PLS-DA model were 0.9680 and 0.08%, respectively. Furthermore, PLSR model was also built using the training set of NIR spectral data to make a regression model. For this PLSR model, leave-one-out cross-validation procedure was used as an internal cross-validation criteria and the R-square and the root mean square error (RMSE) values for the PLSR model were found as 0.9800 and 0.56%, respectively. The PLSR model was then externally validated using a test set. The root means square error of prediction (RMSEP) obtained was 0.48%. The present proposed study was intended to contribute toward the development of a robust, sensitive, and reproducible method to detect and determine the urea adulterant concentration in fresh milk samples.

Rapid Analysis of Milk Using Low-Cost Pocket-Size NIR Spectrometers and Multivariate Analysis.

The miniaturisation of analytical devices, reduction of analytical data acquisition time, or the reduction of waste generation throughout the analytical process are important requirements of modern analytical chemistry, and in particular of green analytical chemistry. Green analytical chemistry has fostered the development of a new generation of miniaturized near-infrared spectroscopy (NIR) spectrometric systems. However, one of the drawbacks of these systems is the need for a compromise between the performance parameters (accuracy and sensitivity) and the aforementioned requirements of green analytical chemistry. In this paper, we evaluated the capabilities of two recently developed portable NIR instruments (SCiO and NeoSpectra) to achieve a rapid, simple and low-cost quantitative determination of commercial milk macronutrients. Commercial milk samples from Italy, Switzerland and Spain were chosen, covering the maximum range of variability in protein, carbohydrate and fat content, and multivariate calibration was used to correlate the recorded spectra with the macronutrient content of milk. Both SCiO and NeoSpectra can provide a fast and reliable analysis of fats in commercial milk, and they are able to correctly classify milk according to fat level. SCiO can also provide predictions of protein content and classification according to presence or absence of lactose.

FT-NIRS Coupled with PLS Regression as a Complement to HPLC Routine Analysis of Caffeine in Tea Samples.

Daily consumption of caffeine in coffee, tea, chocolate, cocoa, and soft drinks has gained wide and plentiful public and scientific attention over the past few decades. The concentration of caffeine in vivo is a crucial indicator of some disorders-for example, kidney malfunction, heart disease, increase of blood pressure and alertness-and can cause some severe diseases including type 2 diabetes mellitus (DM), stroke risk, liver disease, and some cancers. In the present study, near-infrared spectroscopy (NIRS) coupled with partial least-squares regression (PLSR) was proposed as an alternative method for the quantification of caffeine in 25 commercially available tea samples consumed in Oman. This method is a fast, complementary technique to wet chemistry procedures as well as to high-performance liquid chromatography (HPLC) methods for the quantitative analysis of caffeine in tea samples because it is reagent-less and needs little or no pre-treatment of samples. In the current study, the partial least-squares (PLS) algorithm was built by using the near-infrared NIR spectra of caffeine standards prepared in tea samples scanned by a Frontier NIR spectrophotometer (L1280034) by PerkinElmer. Spectra were collected in the absorption mode in the wavenumber range of 10,000-4000 cm-1, using a 0.2 mm path length and CaF2 sealed cells with a resolution of 2 cm-1. The NIR results for the contents of caffeine in tea samples were also compared with results obtained by HPLC analysis. Both techniques provided good results for predicting the caffeine contents in commercially available tea samples. The results of the proposed study show that the suggested FT-NIRS coupled with PLS regression algorithun has a high potential to be routinely used for the quick and reproducible analysis of caffeine contents in tea samples. For the NIR method, the limit of quantification (LOQ) was estimated as 10 times the error of calibration (root mean square error of calibration (RMSECV)) of the model; thus, RMSEC was calculated as 0.03 ppm and the LOQ as 0.3 ppm.

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.

Fast detection and quantification of pork meat in other meats by reflectance FT-NIR spectroscopy and multivariate analysis.

This study aimed to develop a fast analytical method, combining near infrared reflectance spectroscopy and multivariate analysis, for detection and quantification of pork meat in other meat samples. A total of 5952 mixture samples from 39 types of meat were prepared in triplicate, with the inclusion of pork at 0%, 1%, 5%, 10%, 30%, 50%, 70%, 90% and 100%. Each sample was scanned using an FT-NIR spectrophotometer in the reflection mode. Spectra were collected in the wavenumber range from 10,000 to 4000 cm-1, at a resolution of 2 cm-1 and a total path length of 0.5 mm. Principal Component Analysis (PCA) revealed the similarities and differences among the various types of meat samples and Partial Least-Squares Discriminant Analysis (PLS-DA) showed a good discrimination between pure and pork-spiked meat samples. A Partial Least-Squares Regression (PLSR) model was built to predict the pork meat contents in other meats, which provided the R2 value of 0.9774 and RMSECV value of 1.08%. Additionally, an external validation was carried out using a test set, providing a rather good prediction error, with an RMSEP value of 1.84%.

Multivariate Classification of Prunus Dulcis Varieties using Leaves of Nursery Plants and Near Infrared Spectroscopy.

The emergence of new almond tree (Prunus dulcis) varieties with agricultural interest is forcing the nursery plant industry to establish quality systems to keep varietal purity in the production stage. The aim of this study is to assess the capability of near-infrared spectroscopy (NIRS) to classify different Prunus dulcis varieties as an alternative to more expensive methods. Fresh and dried-powdered leaves of six different varieties of almond trees of commercial interest (Avijor, Guara, Isabelona, Marta, Pentacebas and Soleta) were used. The most important variables to discriminate between these varieties were studied through of three scientifically accepted indicators (Variable importance in projection¸ selectivity ratio and vector of the regression coefficients). The results showed that the 7000 to 4000 cm-1 range contains the most useful variables, which allowed to decrease the complexity of the data set. Concerning to the classification models, a high percentage of correct classifications (90-100%) was obtained, where dried-powdered leaves showed better results than fresh leaves. However, the classification rate of both kinds of leaves evidences the capacity of the near-infrared spectroscopy to discriminate Prunus dulcis varieties. We demonstrate with these results the capability of the NIRS technology as a quality control tool in nursery plant industry.

Development of a methodology to analyze leaves from Prunus dulcis varieties using near infrared spectroscopy.

Near-infrared spectroscopy (NIRS) can be a faster and more economical alternative to traditional methods for screening varietal mixtures of nursery plants during the propagation process to ensure varietal purity and to avoid errors in the dispatch batches. The global objective of this work was to develop and optimize a NIR spectral collection method for construction of robust multivariate discrimination models. Three different varieties of Prunus dulcis (Avijor, Guara, and Pentacebas) of agricultural interest were used for this study. Sources of variation were investigated, including the position of the leaves on the trees, differences among trees of the same variety, and differences at the varietal level. Three types of processed samples were investigated. Fresh leaves, dried leaves, and dried leaves in powder form were included in each analysis. A study of spectral pre-treatment methods was also performed, and multivariate methods were applied to analyze the influence of different factors on classification. These included principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and ANOVA simultaneous component analysis (ASCA). The results indicated that variety was the most important factor for classification. The spectral pre-treatment that provided the best results was a combination of standard normal variate (SNV), Savitzky-Golay first derivative, and mean-centering methods. With regard to the type of processed sample, the highest percentages of correct classifications were obtained with fresh and dried powdered leaves at both the training set and test set validation levels. This study represents the first step towards the consolidation of NIRS as a method to identify Prunus dulcis varieties.