The Prediction of Quality Parameters of Craft Beer with FT-MIR and Chemometrics.

Beer is one of the oldest and most known alcoholic beverages whose organoleptic characteristics are the attributes that the consumer seeks, which is why it is essential to ensure proper quality control of the final product. Fourier transform mid-infrared (FT-MIR) spectroscopy coupled with multivariate analysis can be an alternative to traditional methods to predict quality parameters in craft beer. This study aims to develop prediction models based on FT-MIR spectroscopy to simultaneously quantify quality parameters (color, specific gravity, alcohol volume, bitterness, turbidity, pH, and total acidity) in craft beer. Additionally, principal component analysis (PCA) was applied, and it was possible to classify craft beer samples according to their style. Partial least squares (PLS1) developed the best predictive model by obtaining higher R2c (0.9999) values and lower standard error of calibration (SEC: 0.01-0.11) and standard error of prediction (SEP: 0.01-0.14) values in comparison to the models developed with the other algorithms. Specific gravity could not be predicted due to the low variability in the values. Validation and prediction with external samples confirmed the predictive capacity of the developed model. By making a comparison to traditional techniques, FT-MIR coupled with multivariate analysis has a higher advantage, since it is rapid (approximately 6 min), efficient, cheap, and eco-friendly because it does not require the use of solvents or reagents, representing an alternative to simultaneously analyzing quality parameters in craft beer.

Identification of Variety and Prediction of Chemical Composition in Cocoa Beans (Theobroma cacao L.) by FT-MIR Spectroscopy and Chemometrics.

Cocoa is rich in polyphenols and alkaloids that act as antioxidants, anticarcinogens, and anti-inflammatories. Analytical methods commonly used to determine the proximal chemical composition of cocoa, total phenols, and antioxidant capacity are laborious, costly, and destructive. It is important to develop fast, simple, and inexpensive methods to facilitate their evaluation. Chemometric models were developed to identify the variety and predict the chemical composition (moisture, protein, fat, ash, pH, acidity, and phenolic compounds) and antioxidant capacity (ABTS and DPPH) of three cocoa varieties. SIMCA model showed 99% reliability. Quantitative models were developed using the PLS algorithm and favorable statistical results were obtained for all models: 0.93 < R2c < 0.98 (R2c: calibration determination coefficient); 0.03 < SEC < 4.34 (SEC: standard error of calibration). Independent validation of the quantitative models confirmed their good predictive ability: 0.93 < R2v < 0.97 (R2v: validation determination coefficient); 0.04 < SEP < 3.59 (SEP: standard error of prediction); 0.08 < % error < 10.35). SIMCA model and quantitative models were applied to five external cocoa samples, obtaining their chemical composition using only 100 mg of sample in less than 15 min. FT-MIR spectroscopy coupled with chemometrics is a viable alternative to conventional methods for quality control of cocoa beans without using reagents, and with the minimum sample preparation and quantity.

Biological functions of peptides from legumes in gastrointestinal health. A review legume peptides with gastrointestinal protection.

Extensively consumed worldwide, legumes such as beans, soybeans, chickpeas, and peas represent a great source of protein. Legume-derived proteins provide bioactive peptides, small sequences of amino acids produced by enzymatic hydrolysis, gastrointestinal digestion, fermentation, or germination. Recent studies showed diverse biological effects of these peptides as antioxidants, antihypertensives, anti-inflammatory, antimicrobial, antithrombotic, antidiabetic, hypocholesterolemic, and even immunomodulators. These beneficial effects aid in preventing and treating chronic illnesses, particularly inflammatory disorders, obesity, and cardiovascular diseases. Thus, this work discusses these biological functions in gastrointestinal digestion health of bioactive peptides obtained from common beans, soybeans, chickpeas, peas, and other legumes. PRACTICAL APPLICATIONS: Knowledge of the nutraceutical properties of legumes can encourage the use of these seeds as ingredients in the development and design of functional foods.

Potential anti-inflammatory effects of legumes: a review.

Legumes are a staple of diets all around the world. In some least developed countries, they are the primary source of protein; however, their beneficial properties go beyond their nutritional value. Recent research has shown that legumes have bioactive compounds like peptides, polyphenols and saponins, which exhibit antioxidant, antihypertensive, anti-inflammatory and other biological activities. Thus, these compounds could be an alternative treatment for inflammatory diseases, in particular, chronic inflammation such as arthritis, obesity and cancer. Nowadays, there is a growing interest in alternative therapies derived from natural products; accordingly, the present review has compiled the bioactive compounds found in legumes that have demonstrated an anti-inflammatory effect in non-clinical studies.

Effect of In Vitro Digestion on the Antioxidant Compounds and Antioxidant Capacity of 12 Plum (Spondias purpurea L.) Ecotypes.

Spondias purpurea L. plum is a source of antioxidant compounds. Nevertheless, once they are consumed and go through the digestive system, these compounds may undergo changes that modify their bioaccessibility. This study aimed to evaluate the effect of in vitro gastrointestinal digestion on the total content of carotenoids (TCC), ascorbic acid (AA), phenolic compounds (TPC), flavonoids (TFC), anthocyanins (TAC), and antioxidant capacity (ABTS, DPPH) of 12 plum Spondias purpurea L. ecotypes. The plum samples were subjected to the InfoGest in vitro digestion model. TCC, AA, TPC, TFC, TAC, ABTS, and DPPH were significantly different (p ≤ 0.05) in each in vitro digestion stage. The gastric stage released the highest content of AA (64.04-78.66%) and TAC (128.45-280.50%), whereas the intestinal stage released the highest content of TCC (11.31-34.20%), TPC (68.61-95.36%), and TFC (72.76-95.57%). Carotenoids were not identified in the gastric stage whilst anthocyanins were lost at the end of the intestinal digestion. At the gastric stage, AA presented a positive and high correlation with ABTS (r: 0.83) and DPPH (r: 0.84), while, in the intestinal stage, TPC and TFC presented positive and high correlation with ABTS (r ≥ 0.8) and DPPH (r ≥ 0.8), respectively.

Extraction and Microencapsulation of Bioactive Compounds from Muicle (Justicia spicigera) and Their Use in the Formulation of Functional Foods.

Bioactive compounds (BC) present in muicle leaves were extracted using the best extraction conditions obtained with a Box-Behnken experimental design, extracting 95% of BC. Microencapsulation of muicle BC was carried out by spray drying using DE10 maltodextrin (MD) and soy protein isolate (SPI) as encapsulating agents. The best conditions for the ethanolic extraction of BC from muicle were 30 °C, 40% aqueous ethanol, and one extraction for 1 h. The best spray drying encapsulating conditions for BC and antioxidant capacity (AC) using MD as an encapsulating agent were: 160-80 °C and 10% MD in the feeding solution, and for SPI: 180-70 °C and 5% SPI in the feeding solution. Microcapsules were added to yogurt and a sensory evaluation and retention of BC during 15-day storage at 4 °C was performed. Sensory evaluation showed that yogurt with added MD microcapsules had better acceptance than that with SPI microcapsules. Based on this, a jelly with added muicle MD microcapsules was also prepared which obtained better acceptance by the judges. At the end of the storage period, yogurt with SPI microcapsules showed better retention of BC and AC than yogurts with MD microcapsules; however, products with MD microcapsules had better acceptance.

Identification and Quantification of Adulterants in Coffee (Coffea arabica L.) Using FT-MIR Spectroscopy Coupled with Chemometrics.

Food adulteration is an illegal practice performed to elicit economic benefits. In the context of roasted and ground coffee, legumes, cereals, nuts and other vegetables are often used to augment the production volume; however, these adulterants lack the most important coffee compound, caffeine, which has health benefits. In this study, the mid-infrared Fourier transform spectroscopy (FT-MIR) technique coupled with chemometrics was used to identify and quantify adulterants in coffee (Coffea arabica L.). Coffee samples were adulterated with corn, barley, soy, oat, rice and coffee husks, in proportions ranging from 1-30%. A discrimination model was developed using the soft independent modeling of class analogy (SIMCA) framework, and quantitative models were developed using such algorithms as the partial least squares algorithms with one variable (PLS1) and multiple variables (PLS2) and principal component regression (PCR). The SIMCA model exhibited an accuracy of 100% and could discriminate among all the classes. The quantitative model with the highest performance corresponded to the PLS1 algorithm. The model exhibited an R2c: ≥ 0.99, standard error of calibration (SEC) of 0.39-0.82, and standard error of prediction (SEP) of 0.45-0.94. The developed models could identify and quantify the coffee adulterants, and it was considered that the proposed methodology can be applied to identify and quantify the adulterants used in the coffee industry.

Microencapsulation of Rambutan Peel Extract by Spray Drying.

Microencapsulation of bioactive compounds (BC) from rambutan peel by spray drying using DE10 maltodextrin as encapsulating agent was performed. The optimal conditions for the ethanolic extraction of BC were 60 °C, with a time of 1 h, 55% aqueous ethanol and three extraction cycles. The best spray drying encapsulating conditions for BC and antioxidant capacity (AC) were: inlet temperature 160 °C, outlet temperature 80 °C, and 10% encapsulating agent concentration in the feeding solution (core:encapsulating agent ratio of 1:4). With these conditions, retention and encapsulation efficiencies obtained were higher than 85%, the water activity value, moisture content and Hausner Index were of 0.25 ± 0.01, 3.95 ± 0.10%, and 1.42 ± 0.00, respectively. The optimized powder presented good solubility and morphological properties, showing microcapsules without ruptures. Based on these results, microencapsulation by spray drying is a viable technique which protects BC of rambutan peel, facilitating its application in the food, pharmaceutical, and cosmetic industries.

Use of ATR-FTIR spectroscopy coupled with chemometrics for the authentication of avocado oil in ternary mixtures with sunflower and soybean oils.

Avocado oil is a high-value and nutraceutical oil whose authentication is very important since the addition of low-cost oils could lower its beneficial properties. Mid-FTIR spectroscopy combined with chemometrics was used to detect and quantify adulteration of avocado oil with sunflower and soybean oils in a ternary mixture. Thirty-seven laboratory-prepared adulterated samples and 20 pure avocado oil samples were evaluated. The adulterated oil amount ranged from 2% to 50% (w/w) in avocado oil. A soft independent modelling class analogy (SIMCA) model was developed to discriminate between pure and adulterated samples. The model showed recognition and rejection rate of 100% and proper classification in external validation. A partial least square (PLS) algorithm was used to estimate the percentage of adulteration. The PLS model showed values of R(2) > 0.9961, standard errors of calibration (SEC) in the range of 0.3963-0.7881, standard errors of prediction (SEP estimated) between 0.6483 and 0.9707, and good prediction performances in external validation. The results showed that mid-FTIR spectroscopy could be an accurate and reliable technique for qualitative and quantitative analysis of avocado oil in ternary mixtures.

FT-MIR and Raman spectroscopy coupled to multivariate analysis for the detection of clenbuterol in murine model.

A fast and simple screening method for the determination of clenbuterol at the ppb level in a murine model was demonstrated by Mid Infrared (MIR) and Raman spectroscopy in conjunction with multivariate analysis. In order to build the calibration models to quantify clenbuterol in rat meat, mixtures of rat meat and clenbuterol were prepared in a range of 5-10,000 ppb. Partial Least Square (PLS) analysis was used to build the calibration model. The results shown that Mid Infrared and Raman spectroscopy were efficient, but Mid Infrared (R(2) = 0.966 and SEC = 0.27) were superior to Raman (R(2) = 0.914 and SEC = 1.167). The SIMCA model developed showed 100% classification rate of rat meat samples with or without clenbuterol. The results were confirmed with contaminated meat samples from animals treated with clenbuterol. Chemometric models represent an attractive option for meat quality screening without sample pretreatments which can identify veterinary medicinal products at the ppb level.