The consumption of culinary herbs and spices has risen in recent decades, driven by a shift towards healthier foods with nutritional benefits. Spices offer nutraceutical properties, as well as flavor and color in global cuisines. This has led to market growth, with herbs and spices being traded globally. They are mainly sold dehydrated, whole, crushed, or in blends, and may or may not undergo an extraction process, making the industry highly profitable and valued. With the growth and appreciation of the culinary herbs and spices market, there are also several cases of poor food quality and safety with various risks inherent to the production chain. Considering this, this review aimed to i) clarify the current scenario of culinary herbs and spices production, ii) their main processing stages, iii) map the main microbiological and physical risks, specifically how physical hazards (such as insects) contribute to microbiological risks along the production, and iv) options for mitigating these risks, including the main quality guidelines, regulation, and monitoring tools to ensure the quality and safety of herbs and spices. Despite technological advances in herb and spice processing, the lack of adherence to quality guidelines, good practices, and regulations increases the risks of contamination. Additionally, discrepancies between these guidelines and local legislation hinder global trade and prevent coordinated actions to address key food safety risks.

Robusta Amazônico coffee is a highly valued specialty coffee with a registered geographical indication, making it a potential target for fraud. In this context, near-infrared (NIR) and ultraviolet-visible (UV-Vis) spectroscopy are highlighted as environmentally friendly analytical techniques with strong potential for verifying authenticity. In this study, 114 samples of Robusta Amazônico green coffee and 108 samples of other coffees (in intact, ground, and extracted forms) were analyzed using both benchtop and handheld NIR instruments, as well as a UV-Vis spectrophotometer. The Data-Driven Soft Independent Modeling of Class Analogy (DD-SIMCA) algorithm was employed for classification, and performance was evaluated based on sensitivity, specificity, and accuracy. Classification accuracy ranged from 95.6 % to 100 %. Notably, the handheld NIR achieved 100 % accuracy and proved to be the most cost-effective tool, highlighting its suitability for verifying the authenticity of Robusta Amazônico green coffee.

Ultrasound-assisted extraction (UAE) offers a faster and eco-friendly alternative for samples prepared for essential mineral determination in foods. This study aims to validate a UAE method for sample preparation to determine Ca, Mg, Zn, Na, and K in organic dairy products using flame atomic absorption spectroscopy (FAAS) and confocal microscopy was used as one parameter to evaluate the efficiency of process. Commercial organic milk, yogurt, and cheese were analyzed. For experiments, a face-centered experimental design to optimize acid volume (5 and 10 mL) and ultrasonic bath temperature (45 and 70 °C) are used. Optimal conditions were identified for each sample type, and a t-test showed no significant differences in mineral content between the UAE (30 min) and traditional wet mineralization in digestion block (4 h) procedures for the majority of samples. The validated method carried out by UAE demonstrated satisfactory linearity (R2 > 0.98), repeatability (RSD < 10

There is a need to develop efficient analytical methods to recognize the origins of coffee beans, especially from large producers such as Brazil, which offers high value-added Geographical Indication (GI) coffees. However, the challenge is not only the need for fast and clean techniques but also understanding how sample preparation and data treatments directly affect the performance of the applied technique. In this study, Near Infrared (NIR) Spectroscopy was combined with Partial Least Squares Discriminant Analysis (PLS-DA) to assess the ability to discriminate green coffee samples with recognized GI (Robusta Amazônico from Rondônia and Conilon from Espírito Santo), examining the influence of sample presentation (ground or whole bean) and spectral pre-processing. The results demonstrated that NIR performed with high efficiency for both whole beans and ground green coffee, achieving 100 % correct prediction. The most effective pre-processing was the combination of the 1st derivative of Savitzky-Golay and Multiplicative Scatter Correction (MSC). This suggests that the technique can be used for rapid discrimination in green coffee trading, with the direct analysis of natural whole beans being much more advantageous, as it avoids milling, which requires liquid nitrogen and a specific mill. Thus, NIR coupled with PLS-DA is a non-invasive, easy-to-operate, low-cost, and sensitive technique that can be applied directly to intact canephora coffee samples.

Rice is considered a staple food in Asia, Africa, and Latin American countries. As interest in healthier lifestyles increases, the market for organic brown rice is growing. Concurrently, there is a rising concern about developing fast and environmentally friendly analytical monitoring tools that can ensure the quality of brown rice by distinguishing between organic and conventional rice grains. In this study, near-infrared spectroscopy (NIRS) spectral data was acquired using three different instruments: benchtop, hand-held, and hyperspectral imaging (NIR-HSI). Partial Least Squares Discriminant Analysis (PLS-DA) was used to build supervised classification models that differentiate at the grain level between organic and conventional brown rice. The final performance of the three technologies tested was satisfactory. The hand-held, benchtop, and NIR-HSI devices achieved sensitivity and specificity rates between 100% and 87.5%, respectively. Among the NIR instruments, the benchtop version demonstrated outstanding results. It is more affordable and simpler than the NIR hyperspectral imaging system, and more informative than the portable NIR, offering a balanced alternative for analysis. This approach allows for efficient differentiation between organic and conventional food compositions, optimizing the use of time, reagents, and instrumentation before employing more sophisticated identification methods.

Specialty green coffee beans have a higher commercial value and some of them have recently been classified in Brazil based on the indication of provenance and denomination of origin. In this context, the classification of the type of coffee bean is still a challenge, using traditional analytical techniques. Thus, alternative analytical techniques, such as ultraviolet–visible spectroscopy (UV–Vis), as non-target analysis can be applied as a quick and reliable method of coffee classification using data science, such as chemometric tools. In the present study, UV–Vis were evaluated as a new strategy for discrimination of green beans of Brazilian specialty canephora coffees with recognized geographical indications (Robusta Amazônico and Conilon from state of Espírito Santo), for the first time. Spectra obtained from the aqueous extract of 221 samples. The Principal Component Analysis (PCA) was performed and subsequently Partial Least Squares with Discriminant Analysis (PLS-DA) model developed. The PCA indicated tendency to group the samples in their respective classes, pointing to the similarities in the spectra of samples of the same origin. The PLS-DA model obtained showed figures of merit values starting at 89.3% in the test set. The VIP scores showed that the variables associated with chlorogenic acids, caffeine and chlorophyll are the most important for differentiating the studied coffees. The results obtained showed that UV–Vis fingerprint − non-targeted analysis associated with PLS-DA is appropriate for the discrimination of green beans of Brazilian specialty coffee from different origins, in a simple way, using common equipment in several laboratories.

Since 1990, new analytical methods have been developed to eliminate or reduce the use of harmful substances to the environment and human health. The traditional methods of analysis normally evaluate a single analyte at a time, require several preparation steps, use toxic reagents, and are time-consuming and harmful to the environment. These methods have been substituted by analytical techniques that do not require sample preparation or the use of toxic solvents. Thus, techniques based on vibrational spectroscopy and methods that use hyperspectral imaging and RGB imaging system along with magnetic resonance spectroscopy are in compliance with the Green Analytical Chemistry (GAC). However, the data generated by these techniques are complex and numerous, requiring the use of chemometric tools to interpret the results. This chapter will address the GAC Principles and Challenges for Food Analysis, the use of green techniques in food analysis, such as Near-Infrared Spectroscopy (NIR) and Medium Infrared Spectroscopy (MIR), coupled with Hyperspectral Images, Red-Green-Blue (RGB) Image, Raman spectroscopy and Nuclear Magnetic Resonance (NMR), which make use of chemometric tools in data analysis and examples of the application of these green technologies in food analysis.

Members within the Fusarium sambucinum species complex (FSAMSC) are able to produce mycotoxins, such as deoxynivalenol (DON), nivalenol (NIV), zearalenone (ZEN) and enniatins (ENNs) in food products. Consequently, alternative methods for assessing the levels of these mycotoxins are relevant for quick decision-making. In this context, qPCR based on key mycotoxin biosynthetic genes could aid in determining the toxigenic fungal biomass, and could therefore infer mycotoxin content. The aim of this study was to verify the use of qPCR as a technique for estimating DON, NIV, ENNs and ZEN, as well as Fusarium graminearum sensu lato (s.l.) and F. poae in barley grains. For this purpose, 53 barley samples were selected for mycobiota, mycotoxin and qPCR analyses. ENNs were the most frequent mycotoxins, followed by DON, ZEN and NIV. 83% of the samples were contaminated by F. graminearum s.l. and 51% by F. poae. Pearson correlation analysis showed significant correlations for TRI12/15-ADON with DON, ESYN1 with ENNs, TRI12/15-ADON and ZEB1 with F. graminearum s.l., as well as ESYN1 and TRI12/NIV with F. poae. Based on the results, qPCR could be useful for the assessment of Fusarium presence, and therefore, provide an estimation of its mycotoxins' levels from the same sample.

Barley is an important crop worldwide, and it can be affected by various fungi, among them Fusarium is one of the most relevant due to the economic losses caused by mycotoxin contamination. Enniantins (ENNs) are one of the emergent group of mycotoxins that have been found in grains around the world. Nowadays, the main analytical tools available to evaluate these contaminants are based on chromatographic techniques that are efficient but time-consuming and expensive. In this context, the present study aimed to assess the performance of near infrared (NIR) spectroscopy to detect and/or classify the enniatin (ENN) content on barley grains. Sixty samples of barley grains from three different regions of Brazil were investigated and the ENN content determined by UPLC-MS/MS. The levels found were then used to develop multivariate models based on infrared spectral data. The results indicated high incidence off ENN presence in the samples (>70 %) and the PLS-DA model determined by NIR data showed adequate values of sensitivity and sensibility (100 % and 94.2 %, respectively) distinguishing between contaminated and non-contaminated barley samples, demonstrating NIR as a promising tool to monitoring this emergent mycotoxin.

Rice beverage is one of the most consumed plant-based milk alternatives and it is considered a healthy food. However, many products have in its composition food additives that could change the beverages characteristics. In this study, for the first time, the applicability of near infrared spectroscopy (NIR) and multivariate analysis were evaluated for additives detection (starch, maltodextrin, polydextrose, soy soluble extract) in powder rice beverages (PRB). The vibrational spectra did not present relevant differences for samples of PRB pure or with additives. An exploratory analysis (PCA) distinguished the samples according the rice brands used for beverage processing. Moreover, PLS-DA methods performed were able to go further the discrimination of rice brands and reach the main differences between a PRB with or without additives, by applying variable selection according the loadings, iPLS and VIP scores. The best models were obtained with MSC + MC and loadings variable selection with sensitivity and specificity higher than 92%. The results showed that NIR associated to chemometric tools was appropriate for discrimination of PRB added or not with additives and could be used to quality control related to beverages composition and safety associated to presence of allergenic, with ability to replace the traditional methods, especially in a screening step.

Bovine cheese whey presents physico-chemical similarity with goat cheese whey, making the occurrence of adulteration of goat dairy beverage by addition of bovine whey possible. This study aimed to develop a green analytical method, using NIR spectroscopy and Data Driven–Soft Independent Modelling of Class Analogies (DD-SIMCA), to verify the authenticity of goat dairy beverage and to detect adulteration with bovine whey. For this, 60 authentic samples of goat dairy beverage were used and 180 samples were adulterated with 10, 25, 50 and 100% bovine whey. The classification model built based on the full spectra (10,000–4000 cm−1) showed 95 and 100% of right assignments for authentic and adulterated samples, respectively. After the variable selection process (5500–4000 cm−1), the results indicated 100% of classification correct for the samples. In this sense, the combination of NIR and class modeling has proved to be efficient for controlling the authenticity of goat dairy beverages.

The development of green analytical techniques for food industry quality control has become an important issue in the context of the fourth industrial revolution. In this sense, near infrared spectroscopy (NIR) and smartphone-based imaging (SBI) were applied to evaluate the bioactive potential of freeze-dried açai pulps. For this purpose, reference results of ninety-six samples were obtained by determining total anthocyanins (TAC), polyphenol content (TPC), and antioxidant capacity (DPPH, ORAC and TEAC) by traditional methods and correlated to NIR spectra and SBI to build predictive models based on partial square least (PLS) regression. In summary, the NIR-PLS models showed better performance for predicting the TAC, TPC and antioxidant capacity of studied samples; considering the parameters of merit, such as coefficient of determination (0.8) and residual prediction deviation (RPD) (2.2) compared to the SBI-PLS models (0.7 and lower 1.5, respectively). The better performance of NIR-PLS could be potentially justified by a higher sensitivity of the NIR equipment than the smartphone images. In conclusion, these results show that the proposed alternative methods are promising tools for the future context of the 4.0 food industry.

Goat dairy products are the target of fraudulent practices due to their high commercial value. This study evaluated the capacity of vibrational spectroscopy Near Infrared (FT-NIR) coupled with chemometric tools to detect goat’s yogurt and cheese adulterated with cow milk. Principal Component Analysis (PCA), Q-control chart and Partial Least Squares-Discriminant Analysis (PLS-DA) were able to distinguish goat’s cheese and yogurt adulterated with 10, 15 and 20 % of cow milk. The Q-control chart was able to discern between both (authentic and adulterated) evaluated products. PLS-DA showed 100 % sensitivity and specificity in discriminating samples of yogurt and goat cheese. The algorithm Interval Partial Least Square (iPLS) reduced the need for 6001 variables to 140 in the yogurt model and 70 variables in the cheese model, without affecting performance quality. FT-NIR demonstrated reliability in evaluating the authenticity of goat products and future studies employing simplified equipment (portable) and on-line and/or in process applicability should be prioritized.

Regarding mycotoxin exposure, weaned infants and young children are a particular vulnerable subgroup when compared to adults, due to their distinct metabolic rates and lower body weight. In this review, the occurrence and co-occurrence of mycotoxins in baby food and values of daily intakes are presented. Since not all mycotoxin content described by occurrence studies are available during the digestion process there is a critical relevance in studies of bioaccessibility. In this context, a summary of the main baby food bioaccessibility assays and their findings are shown and discussed. In addition, general aspects of the most recent advances in strategies to reduce the bioaccessibility of mycotoxin are also presented.

Nas últimas décadas surgiu o interesse na implementação de ferramentas sustentáveis dentro da cadeia produtiva industrial e, no segmento alimentício, não têm sido diferente. No entanto, o controle de qualidade, necessário para a garantia de adequação dos produtos aos parâmetros físico-químicos determinados pela legislação, assim como, às expectativas do consumidor final, ainda é tradicionalmente realizado através de métodos analíticos que geram resíduos tóxicos. Nesse contexto, métodos analíticos verdes e sustentáveis, como técnicas de espectroscopia vibracional no infravermelho próximo e médio ou técnicas de imagem, têm sido aplicadas recentemente, com sucesso na indústria de alimentos e apresentam ainda um grande potencial de uso. Essas técnicas alternativas atendem às demandas e princípios estabelecidos pela quarta revolução industrial ou indústria 4.0. Sendo assim, essa revisão teve como foco principal consolidar o conhecimento sobre os fundamentos básicos das principais técnicas analíticas verdes usadas na indústria de alimentos, e além disso, mostrar exemplos de como as mesmas vêm sendo aplicadas para a garantia de qualidade em casos de parâmetros físico-químicos, em relação a quantificação de compostos bioativos e também para a certificação da autenticidade dos alimentos.

The current study investigated the fungal diversity in freshly harvested oat samples from the two largest production regions in Brazil, Paraná (PR) and Rio Grande do Sul (RS), focusing primarily on the Fusarium genus and the presence of type B trichothecenes. The majority of the isolates belonged to the Fusarium sambucinum species complex, and were identified as F. graminearum sensu stricto (s.s.), F. meridionale, and F. poae. In the RS region, F. poae was the most frequent fungus, while F. graminearum s.s. was the most frequent in the PR region. The F. graminearum s.s. isolates were 15-ADON genotype, while F. meridionale and F. poae were NIV genotype. Mycotoxin analysis revealed that 92% and 100% of the samples from PR and RS were contaminated with type B trichothecenes, respectively. Oat grains from PR were predominantly contaminated with DON, whereas NIV was predominant in oats from RS. Twenty-four percent of the samples were contaminated with DON at levels higher than Brazilian regulations. Co-contamination of DON, its derivatives, and NIV was observed in 84% and 57.7% of the samples from PR and RS, respectively. The results provide new information on Fusarium contamination in Brazilian oats, highlighting the importance of further studies on mycotoxins.

This study aimed at developing control charts and classification models to investigate sugar and water addition in guava pulp applying near- and mid-infrared (NIR and MIR) spectroscopies and low-level data fusion to compare performance of them. The pulp was produced in a pilot plant (authentic samples) during the harvest season in São Paulo (Brazil), and part of samples was adulterated with sugar or water. Authentic and adulterated samples were analyzed by NIR and MIR. The spectra data obtained were preprocessed, and the principal component analysis was applied. MIR spectra data presents a fingerprint region, which is an important tool to differ authentic and adulterated samples. Control charts and classification models (SIMCA, k-NN, and PLS-DA), which were authenticated by external validation, were used to discriminate authentic from adulterated samples (sugar or water in different concentrations). It was possible to differentiate adulterated from authentic samples through control charts, except for water-adulterated samples using NIR spectral. The models presented excellent values of sensitivity, specificity, accuracy, and efficiency. However, k-NN presented better performance. The results obtained by data fusion presented worse performance than the models based in only one of the techniques. Therefore, these results suggested that NIR and MIR techniques can be used for adulteration detection; however, MIR control charts and k-NN models are more effective to detect sugar or water adulteration in guava pulp.

Consumption of plant-based beverages (PBB) is a growing trend; and have been used as viable substitutes for dairy based products. To date, no study has comparatively analyzed mineral composition and effect of in vitro digestion on the bioaccessibility of different PBB. The aim of this research was to investigate the content of essential minerals (calcium (Ca), magnesium (Mg), iron (Fe), zinc (Zn)) and to estimate the effect of in vitro digestion in plant-based beverages, and their antioxidant bioactive compounds (phenolic compounds and antioxidant capacity). Moreover, the presence of antinutritional factors, such as myo-inositol phosphates fractions, were evaluated. Samples of PBB (rice, cashew nut, almond, peanut, coconut, oat, soy, blended or not with another ingredients, fortified with minerals or naturally present) and milk for comparison were evaluated. TPC ranged from 0.2 mg GAEq/L for coconut to 12.4 mg GAEq/L for rice and, the antioxidant capacity (DPPH) ranged from 3.1 to 306.5 mu mol TE/L for samples containing peanut and oat, respectively. Only a few samples presented myo-inositol phosphates fractions in their composition, mostly IPS and IP6, especially cashew nut beverages. Mineral content showed a wide range for Ca, ranging from 10 to 1697.33 mg/L for rice and coconut, respectively. The Mg content ranged from 6.29 to 251.23-268.43 mg/L for rice and cashew nut beverages, respectively. Fe content ranged from 0.76 mg/L to 12.89 mg/L for the samples of rice. Zinc content ranged from 0.57 mg/L to 8.13 mg/L for samples of oat and soy, respectively. Significant variation was observed for Ca (8.2-306.6 mg/L) and Mg (1.9-107.4 mg/L) dialyzed between the beverages, with lower concentrations of Fe (1.0 mg/L) and Zn (0.5 mg/L) in dialyzed fractions. This study provides at least 975 analytically determined laboratory results, providing important information for characterization and comparison of different plant-based beverages.