A review of Fourier Transform Infrared (FTIR) spectroscopy used in food adulteration and authenticity investigations

Machine learning-assisted Fourier transform infrared spectroscopy to predict adulteration in coriander powder

Coriander is a widely used spice, valued for its flavor, aroma, and nutritional benefits in various cuisines and food products. However, adulteration, such as the addition of sawdust, poses significant risks to food safety and authenticity. This study aims to present a solution for predicting sawdust adulteration in coriander powder by providing a detailed methodology for utilizing machine learning-assisted FTIR spectroscopy. It employs various base models, including linear regression (LR), decision tree (DT), support vector regression (SVR), and artificial neural network, (ANN), for adulteration detection. It was observed that the original dataset and Savitzky-Golay smoothed dataset (dataset generated after preprocessing) yielded superior results by achieving R2 values exceeding 0.92 and 0.96, respectively, for the validation set. It shows that more than 92 % of the variability observed in the adulteration detection is explained by the optimized ANN model due to complex non-linear relationship of adulteration level and spectral features. These findings highlight the potential of machine learning-assisted FTIR spectroscopy in accurately predicting sawdust adulteration in coriander powder. This offers promising prospects for enhancing food authentication practices by quantification of adulteration levels. The study also gives directions and methodology to quantify different types of adulterants in food products using machine learning-assisted FTIR spectroscopy, which can enhance food safety.

All-mirror wavefront division interferometer for Fourier transform spectrometry across multiple spectral ranges

We report on the design of an all-mirror wavefront-division interferometer capable of spectroscopic studies across multiple spectral ranges-from the plasma frequencies of metals to terahertz wavelengths and beyond. The proposed method leverages the properties of laser sources with high spatial coherence. A theoretical framework for the interferometer scheme is presented, along with an analytical solution for determining the far-field interference pattern, which is validated through both optical propagation simulations and experimental results. The practical implementation of the spectrometer, using cost-effective off-the-shelf components (knife-edge prisms for separation and recombination), is demonstrated. The system features ultra-broad optical bandwidth, high throughput, simple architecture, dispersion-free operation, and variable arm split ratio. These unique attributes make our approach a prospective alternative to standard Fourier transform spectrometer schemes, specifically tailored to laser-based scenarios. Further, the employed design inherently enables the measurement of the sample's dispersion. In the experimental section, we demonstrate the feasibility of spectroscopic measurements by coupling the system with a supercontinuum source with more than an octave-spanning range (1.5 µm - 4.4 µm). As a proof-of-concept, an experimental demonstration is provided for various applied spectroscopic studies: transmission measurements of polymers (polypropylene) and gas (methane), as well as reflectance measurements of dried pharmaceuticals (insulin products on a metal surface).

Multianalyte Detection with Metasurface-Based Midinfrared Microspectrometer

Midinfrared (2.5-25 μm) spectroscopy is an ideal tool for identifying chemicals in a nondestructive manner. The traditional platform is a Fourier transform infrared (FTIR) spectrometer, but this is too bulky, expensive, and power-hungry for many applications. There is therefore a growing demand for small, lightweight, and cost-effective microspectrometers for use in the field. One emerging platform is the filter-array detector-array microspectrometer. It pairs a broadband detector array with a thin and rigid array of spectral filters to offer a robust, compact platform for real-time in situ sensing. However, most demonstrations have only focused on identifying a single chemical against a null sample, even though many applications would involve multianalyte detection. In this work, we show a rare attempt at simultaneously tracking multiple analytes with a metasurface filter-array microspectrometer. The metasurface consists of periodic lattices of subwavelength circular apertures in an aluminum layer to create an array of bandpass filters. The filter array is imaged with an off-the-shelf microbolometer via a reverse-lens imaging setup to simultaneously monitor the concentration of ethanol and methanol in gasoline. This represents an important application of fuel quality monitoring. Chemometric models (PLS and SVR) are trained and tested on gasoline blends with ethanol and methanol contents, both ranging from 0% to 20% v/v. A support vector machine regression (SVR) model with a cubic kernel was found to have the lowest combined prediction errors. The root-mean-square-error of prediction (RMSEP) for ethanol and methanol are 1.23% and 1.84% v/v; the corresponding pseudounivariate limit of detection is found to be 4.22% and 6.86% v/v, respectively. This work takes the emerging field of metasurface-based mid-infrared spectrometers from single- to multianalyte detection, thereby considerably expanding their range of potential applications.

Reports of tropane alkaloid poisonings and analytical techniques for their determination in food crops and products from 2013 to 2023

Food safety is crucial to attaining food security and sustainability. Unsafe foods for human and animal consumption lead to product recalls and rejection, negatively impacting the global economy and trade. Similarly, climate change can adversely affect the availability of safe and nutritious food at the table. The changing climatic conditions and global food trade and transport can make the movement of toxic plants possible, resulting in food crops being increasingly invaded by some species of plants that produce toxic secondary metabolites, such as tropane alkaloids (TAs). Datura stramonium from the Solanaceae plant family is an invasive and virulent plant that produces high amounts of two TAs, atropine and scopolamine. Various food poisoning events following accidental or deliberate ingestion of foods contaminated by atropine and scopolamine from seeds of D. stramonium have been recorded in different locations globally. Due to these incidents, regulatory agencies require the development of plant toxin detection methods that can be used in the food chain as early as possible. This systematic review thus focuses on the TA determination techniques in food and feeds published between 2013 and 2023. A particular focus was given to the sample preparation methods, the improvements of each technique claimed, and data to support the performance of each method, especially the ability to measure at or below the maximum level. The review concludes with other technological advancements, including rapid spectroscopy, electrophoresis, and colorimetric methods, as well as the possibility of coupling with smartphones for use in on-farm detection and the challenges in applying them.

A review of the emerging technologies and systems to mitigate food fraud in supply chains

Food fraud has serious consequences including reputational damage to businesses, health and safety risks and lack of consumer confidence. New technologies targeted at ensuring food authenticity has emerged and however, the penetration and diffusion of sophisticated analytical technologies are faced with challenges in the industry. This review is focused on investigating the emerging technologies and strategies for mitigating food fraud and exploring the key barriers to their application. The review discusses three key areas of focus for food fraud mitigation that include systematic approaches, analytical techniques and package-level anti-counterfeiting technologies. A notable gap exists in converting laboratory based sophisticated technologies and tools in high-paced, live industrial applications. New frontiers such as handheld laser-induced breakdown spectroscopy (LIBS) and smart-phone spectroscopy have emerged for rapid food authentication. Multifunctional devices with hyphenating sensing mechanisms together with deep learning strategies to compare food fingerprints can be a great leap forward in the industry. Combination of different technologies such as spectroscopy and separation techniques will also be superior where quantification of adulterants are preferred. With the advancement of automation these technologies will be able to be deployed as in-line scanning devices in industrial settings to detect food fraud across multiple points in food supply chains.

Development of an in-situ forming collagen-based hydrogel as a regenerative bioadhesive for corneal perforations

Corneal injuries play a significant role in global visual impairment, underscoring the demand for innovative biomaterials with specific attributes such as adhesion, cohesion, and regenerative potential. In this study, we have developed a biocompatible bioadhesive for corneal reconstruction. Derived from Collagen type I, naturally present in human corneal stromal tissue, the bioadhesive was cross-linked with modified polyethylene glycol diacrylate (PEGDA-DOPA), rendering it curable through visible light exposure and exhibiting superior adhesion to biological tissues even in wet conditions. The physicochemical characteristics of the proposed bioadhesive were customized by manipulating the concentration of its precursor polymers and adjusting the duration of photocrosslinking. To identify the optimal sample with maximum adhesion, mechanical strength, and biocompatibility, characterization tests were conducted. The optimal specimen, consisting of 30 % (w/v) PEGDA-DOPA and cured with visible light for 5 min, exhibited commendable adhesive strength of 783.6 kPa and shear strength of 53.7 kPa, surpassing that of commercialized eye adhesives.Additionally, biocompatibility test results indicated a notably high survival rate (>100 %) of keratocytes seeded on the hydrogel adhesive after 7 days of incubation. Consequently, this designed bioadhesive, characterized by high adhesion strength, robust mechanical strength, and excellent biocompatibility, is anticipated to enhance the spontaneous repair process of damaged corneal stromal tissue.

Advancing ethanol content determination in hydrogels: non-destructive and operational methods for health and criminal inspections

The significance of accurate determination of ethanol content in hydrogel formulations was accentuated during COVID-19 pandemic coinciding with the heightened demand for sanitizing agents. The present article proposes three robust methodologies for this purpose: Fourier Transform Infrared Spectroscopy (FTIR), Raman spectroscopy, and Densitometry with matrix effect correction by Near-Infrared Spectroscopy (NIR). All three methods demonstrated outstanding linearity (R2 ≥ 0.99) and minimal errors (< 1.7%), offering simplicity and operational efficiency. FTIR and Raman, being non-destructive and requiring minimal preparation, enable practical on-site analysis capabilities, underscoring the potential of the spectroscopic methods to expedite health investigations and inspections, empowering on-site ethanol determination, and relieving the burden on official laboratories. Additionally, the densitometry with NIR-based approach showcased superior accuracy and precision compared to spectroscopic methods, meeting validation criteria while offering operational advantages over the costly official distillation-based method. Therefore, it stands as a reliable and reproducible technique for comprehensive health and criminal compliance assessments, making it a compelling alternative for both industry and official laboratories.

A modified sequential wavenumber selection-discriminant analysis with Bayesian optimization strategy for detection and identification of chia seed oil adulteration using Raman spectroscopy

The detection of oil fraud can be accomplished through the use of Raman spectroscopy, which is a potent analytical technique for identifying the adulteration of edible oils with inferior or less expensive oils. However, appropriate data reduction and classification methods are required to achieve high accuracy and reliability in the analysis of Raman spectra. In this study, data reduction algorithms such as principal component analysis (PCA) and modified sequential wavenumber selection (MSWS) were applied, along with discriminant analysis (DA) as a classifier for detecting oil fraud. The parameters of DA, such as the discriminant type, the amount of regularization, and the linear coefficient threshold, were optimized using Bayesian optimization. The methods were tested on a dataset of chia oil mixed with 5-40 % sunflower oil, which is a common form of fraud in the market. The results showed that MSWS-DA achieved 100 % classification accuracy, while PCA-DA achieved 91.3 % accuracy. Therefore, it was demonstrated that Raman spectroscopy combined with MSWS-DA and Bayesian optimization can effectively detect oil fraud with high accuracy and robustness.

Advanced detection tools in food fraud: A systematic review for holistic and rational detection method based on research and patents

Modern food chain supply management necessitates the dire need for mitigating food fraud and adulterations. This holistic review addresses different advanced detection technologies coupled with chemometrics to identify various types of adulterated foods. The data on research, patent and systematic review analyses (2018-2023) revealed both destructive and non-destructive methods to demarcate a rational approach for food fraud detection in various countries. These intricate hygiene standards and AI-based technology are also summarized for further prospective research. Chemometrics or AI-based techniques for extensive food fraud detection are demanded. A systematic assessment reveals that various methods to detect food fraud involving multiple substances need to be simple, expeditious, precise, cost-effective, eco-friendly and non-intrusive. The scrutiny resulted in 39 relevant experimental data sets answering key questions. However, additional research is necessitated for an affirmative conclusion in food fraud detection system with modern AI and machine learning approaches.

Rapid detection and quantification of melamine, urea, sucrose, water, and milk powder adulteration in pasteurized milk using Fourier transform infrared (FTIR) spectroscopy coupled with modern statistical machine learning algorithms

There is an evident requirement for a rapid, efficient, and simple method to screen the authenticity of milk products in the market. Fourier transform infrared (FTIR) spectroscopy stands out as a promising solution. This work employed FTIR spectroscopy and modern statistical machine learning algorithms for the identification and quantification of pasteurized milk adulteration. Comparative results demonstrate modern statistical machine learning algorithms will improve the ability of FTIR spectroscopy to predict milk adulteration compared to partial least square (PLS). To discern the types of substances utilized in milk adulteration, a top-performing multiclassification model was established using multi-layer perceptron (MLP) algorithm, delivering an impressive prediction accuracy of 97.4 %. For quantification purposes, bayesian regularized neural networks (BRNN) provided the best results for the determination of both melamine, urea and milk powder adulteration, while extreme gradient boosting (XGB) and projection pursuit regression (PPR) gave better results in predicting sucrose and water adulteration levels, respectively. The regression models provided suitable predictive accuracy with the ratio of performance to deviation (RPD) values higher than 3. The proposed methodology proved to be a cost-effective and fast tool for screening the authenticity of pasteurized milk in the market.

Geographical discrimination of Paw San rice cultivated in different regions of Myanmar using near-infrared spectroscopy, headspace-gas chromatography-ion mobility spectrometry and chemometrics

Paw San rice, also known as "Myanmar pearl rice", is considered the highest quality rice in Myanmar. There are considerable differences in terms of the premium commercial value of Paw San rice, which is an incentive for fraud, e.g. adulteration with cheaper rice varieties or mislabelling its geographical origin. Shwe Bo District is one of the most popular rice growing areas in the Sagaing region of Myanmar which produces the most valued and highly priced Paw San rice (Shwe Bo Paw San). The verification of the geographical origin of Paw San rice is not readily undertaken in the rice supply chain because the existing analytical approaches are time-consuming and expensive. Therefore, there is a need for rapid, robust and cost-effective analytical techniques for monitoring the authenticity and geographical origin of Paw San rice. In this 4-year study, two rapid screening techniques, Fourier-transform near-infrared (FT-NIR) spectroscopy and headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS), coupled with chemometric modelling, were applied and compared for the regional differentiation of Paw San rice. In addition, low-level fusion of the FT-NIR and HS-GC-IMS data was performed and its effect on the discriminative power of the chemometric models was assessed. Extensive model validation, including the validation using independent samples from a different production year, was performed. Furthermore, the effect of the sample preparation technique (grinding versus no sample preparation) on the performance of the discriminative model, obtained with FT-NIR spectral data, was assessed. The study discusses the suitability of FT-NIR spectroscopy, HS-GC-IMS and the combination of both approaches for rapid determination of the geographical origin of Paw San rice. The results demonstrated the excellent potential of the FT-NIR spectroscopy as well as HS-GC-IMS for the differentiation of Paw San rice cultivated in two distinct geographical regions. The OPLS-DA model, built using FT-NIR data of rice from 3 production years, achieved 96.67% total correct classification rate of an independent dataset from the 4th production year. The DD-SIMCA model, built using FT-NIR data of ground rice, also demonstrated the highest performance: 94% sensitivity and 97% specificity. This study has demonstrated that FT-NIR spectroscopy can be used as an accessible, rapid and cost-effective screening tool to discriminate between Paw San rice cultivated in the Shwe Bo and Ayeyarwady regions of Myanmar.

Application of the FTIR technique as a non-invasive tool to discriminate Portuguese olive oils with Protected Designation of Origin

Three Portuguese olive oils with PDO ('Azeite do Alentejo Interior', 'Azeites da Beira Interior' and 'Azeite de Trás-os-Montes') were studied considering their physicochemical quality, antioxidant capacity, oxidative stability, total phenols content, gustatory sensory sensations and Fourier transform infrared (FTIR) spectra. All oils fulfilled the legal thresholds of EVOOs and the PDO's specifications. Olive oils from 'Azeite da Beira Interior' and 'Azeite de Trás-os-Montes' showed greater total phenols contents and antioxidant capacities, while 'Azeites da Beira Interior' presented higher oxidative stabilities. Linear discriminant models were developed using FTIR spectra (transmittance and the 1st and 2nd derivatives), allowing the correct identification of the oils' PDO (100 % sensitivity and specificity, repeated K-fold-CV). This study also revealed that multiple linear regression models, based on FTIR transmittance data, could predict the sweet, bitter, and pungent intensities of the PDO oils (R2 ≥0.979±0.016; RMSE≤0.26±0.05, repeated K-fold-CV). This demonstrates the potential of using FTIR as a non-destructive technique for authenticating oils with PDO.

Exploring the apoptotic effects of sericin on HCT116 cells through comprehensive nanostring transcriptomics and proteomics analysis

Sericin, a silk protein from Bombyx mori (silkworms), has many applications, including cosmetics, anti-inflammation, and anti-cancer. Sericin complexes with nanoparticles have shown promise for breast cancer cell lines. Apoptosis, a programmed cell death mechanism, stops cancer cell growth. This study found that Sericin urea extract significantly affected HCT116 cell viability (IC50 = 42.00 ± 0.002 µg/mL) and caused apoptosis in over 80% of treated cells. S-FTIR analysis showed significant changes in Sericin-treated cells' macromolecule composition, particularly in the lipid and nucleic acid areas, indicating major cellular modifications. A transcriptomics study found upregulation of the apoptotic signaling genes FASLG, TNFSF10, CASP3, CASP7, CASP8, and CASP10. Early apoptotic proteins also showed that BAD, AKT, CASP9, p53, and CASP8 were significantly upregulated. A proteomics study illuminated Sericin-treated cells' altered protein patterns. Our results show that Sericin activated the extrinsic apoptosis pathway via the caspase cascade (CASP8/10 and CASP3/7) and the death receptor pathway, involving TNFSF10 or FASLG, in HCT116 cells. Upregulation of p53 increases CASP8, which activates CASP3 and causes HCT116 cell death. This multi-omics study illuminates the molecular mechanisms of Sericin-induced apoptosis, sheds light on its potential cancer treatment applications, and helps us understand the complex relationship between silk-derived proteins and cellular processes.

Peptides with Charged Amino Acids Mitigate nZnO-Induced Growth Inhibition of Lactobacillus rhamnosus LRa05

Growing concern is about the potential side effects of nanomaterials from food packaging, notably zinc oxide nanoparticles (nZnO). Previous research revealed that walnut-derived peptides could mitigate this inhibitory effect, but the mechanism involved is unclear. Here, we found that not all peptides have such an effect. Based on the growth inhibition model of Lactobacillus rhamnosus LRa05 induced by nZnO, we assessed the protective effects of various peptides. Notably, four peptides containing charged amino acids (PPKNW, WPPKN, ADIYTE, and WEREEQE) were found to effectively alleviate the growth inhibition phenomenon. We hypothesize that the peptide-nZnO interaction modifies this effect, as confirmed through infrared, Raman, and fluorescence spectroscopy. Our results highlight amide bonds, amino groups, carboxyl groups, and benzene rings as key peptide binding sites on nZnO, with static quenching primarily due to hydrogen bonds and van der Waals forces. This study elucidates peptide characteristics in nZnO interactions, facilitating a deeper exploration of food matrix-nanocomposite interactions.

Performance of benchtop and portable spectroscopy equipment for discriminating Iberian ham according to breed

Iberian ham is a highly appreciated product and according to Spanish legislation different labels identify different products depending on the genetic purity. Consequently, "100% Iberian" ham from purebred Iberian animals is more expensive than "Iberian" ham from Iberian x Duroc crosses. The hypothesis of this study was that to avoid labelling fraud it is possible to distinguish the breed (Iberian or Iberian x Duroc) of acorn-fed pigs of Iberian ham without any prior preparation of the sample by using spectroscopy that is a rapid and reliable technology. Moreover, portable devices which can be used in situ could provide similar results to those of benchtop equipment. Therefore, the spectra of the 60 samples (24 samples of 100% Iberian ham and 36 samples of Iberian x Duroc crossbreed ham) were recorded only for the fat, only for the muscle, or for the whole slice with two benchtop near-infrared (NIR) spectrometers (Büchi NIRFlex N-500 and Foss NIRSystem 5000) and five portable spectrometers including four portable NIR devices (VIAVI MicroNIR 1700 ES, TellSpec Enterprise Sensor, Thermo Fischer Scientific microPHAZIR, and Consumer Physics SCiO Sensor), and one RAMAN device (BRAVO handheld). The results showed that, in general, the whole slice recording produced the best results for classification purposes. The SCiO device showed the highest percentages of correctly classified samples (97% in calibration and 92% in validation) followed by TellSpec (100% and 81%). The SCiO sensor also showed the highest percentages of success when the analyses were performed only on lean meat (97% in calibration and 83% in validation) followed by microPHAZIR (84% and 81%), while in the case of the fat tissue. Raman technology showed the best discrimination capacity (96% and 78%) followed by microPHAZIR (89% and 81%). Therefore, spectroscopy has proved to be a suitable technology for discriminating ham samples according to breed purity; portable devices have been shown to give even better results than benchtop spectrometers.

Characterization of Moringa oleifera Seed Oil for the Development of a Biopackage Applied to Maintain the Quality of Turkey Ham

Moringa oleifera has a high level of active chemicals that are useful in the food industry, and they have antibacterial and food preservation properties. The characterization of M. oleifera seed oil (MOS) may vary due to agronomic and environmental factors. Therefore, it was necessary to know the composition of lipids present in our oil extracted under pressing at 180 °C and thus determine if it is suitable to produce a biopackaging. Within the characterization of the oil, it was obtained that MOS presented high-quality fatty acids (71% oleic acid) with low values of acidity (0.71 mg KOH/g) and peroxide (1.74 meq O2/kg). Furthermore, MOS was not very sensitive to lipoperoxidation by tert-butyl hydroperoxide (tBuOOH) and its phenolic components, oleic acid and tocopherols, allowed MOS to present a recovery of 70% after 30 min of treatment. Subsequently, a biopackaging was developed using a multiple emulsion containing corn starch/carboxymethylcellulose/glycerol/MOS, which presented good mechanical properties (strength and flexibility), transparency, and a barrier that prevents the transfer of UV light by 30% and UV-C by 98%, as well as a flux with the atmosphere of 5.12 × 10-8 g/ m.s. Pa that prevents moisture loss and protects the turkey ham from O2. Hence, the turkey ham suffered less weight loss and less hardness due to its preservation in the biopackaging.

Selected food items adulteration, their impacts on public health, and detection methods: A review

Every living thing requires food to survive. Clean, fresh, and healthy foods are important to human health. Today, food is affected by various counterfeits. Adulteration of food is the intentional deterioration of the quality of food offered for sale by either the addition or substitution of an inferior substance or by the omission of a valuable ingredient. Economically motivated adulteration is the intentional adulteration of food for financial gain, and has enormous public health implications, making it an important issue in food science. Almost every food, including milk and dairy products, fats and oils, fruits and vegetables, grain foods, coffee, tea, honey, etc., is susceptible to adulteration. It is difficult to find food that is free from adulteration. Consumption of adulterated food contributes to numerous diseases in society, ranging from mild to life threatening. Therefore, detection of adulteration in food is essential to ensure the safety of the food we consume. To provide consumers with food that is free of adulterants, various detection methods such as physical, chemical, biochemical, and molecular techniques are used to identify adulterants in food. This review aims to provide up-to-date information on food adulteration, its impact on health, and the analytical techniques used to detect adulteration in food.

Algae Food Products as a Healthcare Solution

Diseases such as obesity; cardiovascular diseases such as high blood pressure, myocardial infarction and stroke; digestive diseases such as celiac disease; certain types of cancer and osteoporosis are related to food. On the other hand, as the world's population increases, the ability of the current food production system to produce food consistently is at risk. As a result, intensive agriculture has contributed to climate change and a major environmental impact. Research is, therefore, needed to find new sustainable food sources. One of the most promising sources of sustainable food raw materials is macroalgae. Algae are crucial to solving this nutritional deficiency because they are abundant in bioactive substances that have been shown to combat diseases such as hyperglycemia, diabetes, obesity, metabolic disorders, neurodegenerative diseases and cardiovascular diseases. Examples of these substances include polysaccharides such as alginate, fucoidan, agar and carrageenan; proteins such as phycobiliproteins; carotenoids such as β-carotene and fucoxanthin; phenolic compounds; vitamins and minerals. Seaweed is already considered a nutraceutical food since it has higher protein values than legumes and soy and is, therefore, becoming increasingly common. On the other hand, compounds such as polysaccharides extracted from seaweed are already used in the food industry as thickening agents and stabilizers to improve the quality of the final product and to extend its shelf life; they have also demonstrated antidiabetic effects. Among the other bioactive compounds present in macroalgae, phenolic compounds, pigments, carotenoids and fatty acids stand out due to their different bioactive properties, such as antidiabetics, antimicrobials and antioxidants, which are important in the treatment or control of diseases such as diabetes, cholesterol, hyperglycemia and cardiovascular diseases. That said, there have already been some studies in which macroalgae (red, green and brown) have been incorporated into certain foods, but studies on gluten-free products are still scarce, as only the potential use of macroalgae for this type of product is considered. Considering the aforementioned issues, this review aims to analyze how macroalgae can be incorporated into foods or used as a food supplement, as well as to describe the bioactive compounds they contain, which have beneficial properties for human health. In this way, the potential of macroalgae-based products in eminent diseases, such as celiac disease, or in more common diseases, such as diabetes and cholesterol complications, can be seen.

Quality Standards and Pharmacological Interventions of Natural Oils: Current Scenario and Future Perspectives

Medicinal plants are rich sources of natural oils such as essential and fixed oils used traditionally for nutritive as well as medicinal purposes. Most of the traditional formulations or phytopharmaceutical formulations contain oil as the main ingredient due to their own therapeutic applications and thus mitigating several pathogeneses such as fungal/bacterial/viral infection, gout, psoriasis, analgesic, antioxidant, skin infection, etc. Due to the lack of quality standards and progressive adulteration in the natural oils, their therapeutic efficacy is continuously deteriorated. To develop quality standards and validate scientific aspects on essential oils, several chromatographic and spectroscopic techniques such as HPTLC, HPLC, NMR, LC-MS, and GC-MS have been termed as the choices of techniques for better exploration of metabolites, hence sustaining the authenticity of the essential oils. In this review, chemical profiling and quality control aspects of essential or fixed oils have been explored from previously reported literature in reputed journals. Methods of chemical profiling, possible identified metabolites in essential oils, and their therapeutic applications have been described. The outcome of the review reveals that GC-MS/MS, LC-MS/MS, and NMR-based chromatographic and spectroscopic techniques are the most liable, economic, precise, and accurate techniques for determining the spuriousness or adulteration of oils based on their qualitative and quantitative chemical profiling studies. This review occupies the extensive information about the quality standards of several oils obtained from natural sources for their regulatory aspects via providing the detailed methods used in chemoprofiling techniques. Hence, this review helps researchers in further therapeutic exploration as well as quality-based standardization for their regulatory purpose.

Possible Alternatives: Identifying and Quantifying Adulteration in Buffalo, Goat, and Camel Milk Using Mid-Infrared Spectroscopy Combined with Modern Statistical Machine Learning Methods

Adulteration of higher priced milks with cheaper ones to obtain extra profit can adversely affect consumer health and the market. In this study, pure buffalo milk (BM), goat milk (GM), camel milk (CM), and their mixtures with 5-50% (vol/vol) cow milk or water were used. Mid-infrared spectroscopy (MIRS) combined with modern statistical machine learning was used for the discrimination and quantification of cow milk or water adulteration in BM, GM, and CM. Compared to partial least squares (PLS), modern statistical machine learning-especially support vector machines (SVM), projection pursuit regression (PPR), and Bayesian regularized neural networks (BRNN)-exhibited superior performance for the detection of adulteration. The best prediction models for the different predictive traits are as follows: The binary classification models developed by SVM resulted in differentiation of CM-cow milk, and GM/CM-water mixtures. PLS resulted in differentiation of BM/GM-cow milk and BM-water mixtures. All of the above models have 100% classification accuracy. SVM was used to develop multi-classification models for identifying the high and low proportions of cow milk in BM, GM, and CM, as well as the high and low proportions of water adulteration in BM and GM, with correct classification rates of 94%, 100%, 100%, 99%, and 100%, respectively. In addition, a PLS-based model was developed for identifying the high and low proportions of water adulteration in CM, with correct classification rates of 100%. A regression model for quantifying cow milk in BM was developed using PCA + BRNN, with RMSEV = 5.42%, and RV2 = 0.88. A regression model for quantifying water adulteration in BM was developed using PCA + PPR, with RMSEV = 1.70%, and RV2 = 0.99. Modern statistical machine learning improved the accuracy of MIRS in predicting BM, GM, and CM adulteration more effectively than PLS.

Comparison of Metabolites and Species Classification of Thirteen Zingiberaceae Spices Based on GC-MS and Multi-Spectral Fusion Technology

Due to a similar plant morphology in the majority of Zingiberaceae spices, substitution and adulteration frequently take place during the sales process. Therefore, it is important to analyze the metabolites and species classification of different Zingiberaceae spices. This study preliminarily explored the differences in the metabolites in thirteen Zingiberaceae spices through untargeted gas chromatography-mass spectrometry (GC-MS) and combined spectroscopy, establishing models for classifying different Zingiberaceae spices. On one hand, a total of 81 metabolites were successfully identified by GC-MS. Thirty-seven differential metabolites were screened using variable important in projection (VIP ≥ 1). However, the orthogonal partial least squares discriminant analysis (OPLS-DA) model established using GC-MS data only explained about 30% of the variation. On the other hand, the partial least squares discriminant analysis (PLS-DA) models with three spectral data fusion strategies were compared, and their classification accuracy reached 100%. Among them, the mid-level data fusion model based on latent variables had the best performance. This study provides a powerful tool for distinguishing different Zingiberaceae spices and assists in reducing the occurrence of substitution and adulteration phenomena.

Effects of Larrea nitida nanodispersions on the growth inhibition of phytopathogens

Larrea nitida Cav. (Zygophyllaceae) is a plant endemic to Argentina and Chile, and its extract has been studied over the last years due to the presence of antimicrobial agents that can be used to control the growth of some pathogens in agriculture. However, the extract is highly hydrophobic, which strongly affects its fungicidal activity in aqueous media. In this sense, the solid dispersion technique was used to produce L. nitida extract nanodispersions with polyethylene glycol (PLE) and with polyethylene glycol and zinc acetate (PZLE). In order to further evaluate the activity of the extract in PLE and PZLE, blank nanodispersions containing only polyethylene glycol (PEG) and zinc acetate (PZ) without the addition of the extract were also produced. The fungicidal activity of the water-soluble nanoparticles was evaluated at different concentrations (0.037-0.110 g.mL-1). In general, the nanoparticles were successfully produced on a nanometric size and presented a significant inhibitory activity on the growth of the pathogens Fusarium oxysporum and Fusarium verticillioides in aqueous media. Compared to PLE, PZLE presented increased fungistatic activity, possibly due to their increased solubility in water. Even though their application in agriculture should be further investigated, the nanodispersions present great potential to be applied as a green biotechnological tool.

Application of ATR-FTIR Incorporated with Multivariate Data Analysis for Discrimination and Quantification of Urea as an Adulterant in UHT Milk

Urea is naturally present in milk, yet urea is added intentionally to increase milk's nitrogen content and shelf life. In this study, a total of 50 Ultra heat treatment (UHT) milk samples were spiked with known urea concentrations (0-5 w/v%). Attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy with principal component analysis (PCA), discriminant analysis (DA), and multiple linear regression (MLR) were used for the discrimination and quantification of urea. The PCA was built using 387 variables with higher FL > 0.75 from the first PCA with cumulative variability (90.036%). Subsequently, the DA model was built using the same variables from PCA and demonstrated the good distinction between unadulterated and adulterated milk, with a correct classification rate of 98% for cross-validation. The MLR model used 48 variables with p-value < 0.05 from the DA model and gave R2 values greater than 0.90, with RMSE and MSE below 1 for cross-validation and prediction. The DA and MLR models were then validated externally using a test dataset, which shows 100% correct classification, and the t-test result (p > 0.05) indicated that the MLR could determine the percentage of urea in UHT milk within the permission limit (70 mg/mL). In short, the wavenumbers 1626.63, 1601.98, and 1585.5534 cm-1 are suitable as fingerprint regions for detecting urea in UHT milk.

Fourier Transform Mid-Infrared Spectroscopy (FT-MIR) as a Method of Identifying Contaminants in Sugar Beet Production Process-Case Studies

Food safety has received considerable attention in recent years. Methods for rapid identification of a variety contaminants in both the final product and the manufacturing process are constantly developing. This study used Fourier Transform Mid-Infrared Spectroscopy (FT-MIR) spectroscopy to identify various contaminants endangering white sugar production. It was demonstrated that inorganic compounds (calcium carbonate-CaCO₃), plastic contaminants (polypropylene), and oily contaminants (compressor sealing and lubrication lubricant) can be identified with a high degree of precision. FT-MIR spectroscopy was proved to be a useful technique for detecting sugar contaminants rapidly and precisely even without the application of a sophisticated spectra analysis. Commercial databases of reference spectra usage significantly simplify and facilitate the application of this method.

Fourier-transform infrared spectroscopy: a universal optical sensing technique with auspicious application prospects in the diagnosis and management of autoimmune diseases

Autoimmune diseases (AIDs) are poorly understood clinical syndromes due to breakdown of immune tolerance towards specific types of self-antigens. They are generally associated with an inflammatory response mediated by lymphocytes T, autoantibodies or both. Ultimately, chronic inflammation culminates in tissue damages and clinical manifestations. AIDs affect 5% of the world population, and they represent the main cause of fatality in young to middle-aged females. In addition, the chronic nature of AIDs has a devastating impact on the patient's quality of life. It also places a heavy burden on the health care system. Establishing a rapid and accurate diagnosis is considered vital for an ideal medical management of these autoimmune disorders. However, for some AIDs, this task might be challenging. Vibrational spectroscopies, and more particularly Fourier-transform infrared (FTIR) spectroscopy, have emerged as universal analytical techniques with promising applications in the diagnosis of various types of malignancies and metabolic and infectious diseases. The high sensitivity of these optical sensing techniques and their minimal requirements for test reagents qualify them to be ideal analytical techniques. The aim of the current review is to explore the potential applications of FTIR spectroscopy in the diagnosis and management of most common AIDs. It also aims to demonstrate how this technique has contributed to deciphering the biochemical and physiopathological aspects of these chronic inflammatory diseases. The advantages that can be offered by this optical sensing technique over the traditional and gold standard methods used in the diagnosis of these autoimmune disorders have also been extensively discussed.

Complexation with Polysaccharides Enhances the Stability of Isolated Anthocyanins

Isolated anthocyanins have limited colonic bioavailability due to their instability as free forms. Thus, many methods have been fabricated to increase the stability of anthocyanins. Complexation, encapsulation, and co-pigmentation with other pigments, proteins, metal ions, and carbohydrates have been reported to improve the stability and bioavailability of anthocyanins. In this study, anthocyanins extracted from purple potatoes were complexed with four different polysaccharides and their mixture. The anthocyanin-polysaccharide complexes were characterized using a zeta potential analyzer, particle size analyzer, scanning electron microscopy, and Fourier-transform infrared spectroscopy. Complexes were subjected to simulated digestion for assessing the stability of anthocyanins. Furthermore, complexes were subjected to different pH conditions and incubated at high temperatures to monitor color changes. A Caco-2 cell monolayer was used to evaluate the colonic concentrations of anthocyanins. In addition, the bioactivity of complexes was assessed using LPS-treated Caco-2 cell monolayer. Results show that pectin had the best complexation capacity with anthocyanins. The surface morphology of the anthocyanin-pectin complex (APC) was changed after complexation. APC was more resistant to the simulated upper gastrointestinal digestion, and high pH and temperature conditions for a longer duration. Furthermore, APC restored the lipopolysaccharide (LPS)-induced high cell permeability compared to isolated anthocyanins. In conclusion, complexation with pectin increased the stability and colonic bioavailability and the activity of anthocyanins.

Rapid Sensing: Hand-Held and Portable FTIR Applications for On-Site Food Quality Control from Farm to Fork

Today, one of the world's biggest problems is the assurance of food integrity from farm to fork. Economically motivated food adulteration and food authenticity problems are increasing daily with considerable health and economic effects. Early detection and prevention of food integrity-related problems could be provided by the application of effective on-site food analysis technologies. FTIR spectroscopy coupled with chemometrics can be used for the rapid quality control of a wide variety of food products with fast, high-throughput, accurate and nondestructive analysis advantages. In particular, hand-held and portable FTIR instruments have the potential to surveil food quality and food safety in various critical segments of the food supply chain. In this review, we explore the abilities of hand-held and portable FTIR spectrometers combined with multivariate statistics to conduct a quality evaluation of various food products in terms of food adulteration and authenticity issues. An examination of the literature showed that comparable results were obtained based on detection limits, correlation coefficient (R²) values, standard error values and discrimination power by using both portable/hand-held FTIR spectrometers and benchtop FTIR spectrometers. In conclusion, this review highlights the potential usefulness of portable and hand-held FTIR spectrometers combined with chemometrics for maintaining the food quality through the presentation of various applications that may shed light for on-site food control at any point of the food supply chain.

Raman Scattering-Based Optical Sensing Of Chronic Liver Diseases

Chronic liver diseases (CLDs) are a major public health problem. Despite the progress achieved in fighting against viral hepatitis, the emergence of non-alcoholic fatty liver disease might pose a serious challenge to the public's health in the coming decades. Medical management of CLDs represents a substantial burden on the public health infrastructures. The health care cost of these diseases is an additional burden that weighs heavily on the economies of developing countries. Effective management of CLDs requires the adoption of reliable and cost-effective screening and diagnosing methods to ensure early detection and accurate clinical assessment of these diseases. Vibrational spectroscopies have emerged as universal analytical methods with promising applications in various industrial and biomedical fields. These revolutionary analytical techniques rely on analyzing the interaction between a light beam and the test sample to generate a spectral fingerprint. This latter is defined by the analyte's chemical structure and the molecular vibrations of its functional groups. Raman spectroscopy and surface-enhanced Raman spectroscopy have been used in combination with various chemometric tests to diagnose a wide range of malignant, metabolic and infectious diseases. The aim of the current review is to cast light on the use of these optical sensing methods in the diagnosis of CLDs. The vast majority of research works that investigated the potential application of these spectroscopic techniques in screening and detecting CLDs were discussed here. The advantages and limitations of these modern analytical methods, as compared with the routine and gold standard diagnostic approaches, were also reviewed in details.

Organosulfur in food samples: Recent updates on sampling, pretreatment and determination technologies

Organosulfur compounds (OSCs), mainly found in garlic, are the main biologically active substances for their pharmacological effects, including lowering of blood pressure and cholesterol, anti-cancer effect, liver protection, and anti-inflammatory. Efficient and sensitive pretreatment and determination methods of OSCs in different food matrices are of great significance. This review provides a comprehensive summary about the pretreatment and determination methods for OSCs in different food samples since 2010. Commonly used pretreatment methods, such as liquid-liquid extraction, microwave-assisted extraction, pressurized liquid extraction, liquid-liquid microextraction, solid phase extraction, dispersive solid phase extraction, solid-phase microextraction, and so on, have been summarized and overviewed in this paper. In particular, we discussed and compared various analysis methods including high performance liquid chromatography coupled with different detectors, gas chromatography-based methods, and few other methods. Finally, we tried to highlight the applicability, advantages and disadvantages of different pretreatment and analysis methods, and identified future prospects in this field.

The Analytical Possibilities of FT-IR Spectroscopy Powered by Vibrating Molecules

This paper discusses the state of advancement in the development of spectroscopic methods based on the use of mid (proper) infrared radiation in the context of applications in various fields of science and technology. The authors drew attention to the most important solutions specific to both spectroscopy itself (ATR technique) and chemometric data processing tools (PCA and PLS models). The objective of the current paper is to collect and consistently present information on various aspects of FT-IR spectroscopy, which is not only a well-known and well-established method but is also continuously developing. The innovative aspect of the current review is to show FT-IR's great versatility that allows its applications to solve and explain issues from both the scientific domain (e.g., hydrogen bonds) and practical ones (e.g., technological processes, medicine, environmental protection, and food analysis). Particular attention was paid to the issue of hydrogen bonds as key non-covalent interactions, conditioning the existence of living matter and determining the number of physicochemical properties of various materials. Since the role of FT-IR spectroscopy in the field of hydrogen bond research has great significance, a historical outline of the most important qualitative and quantitative hydrogen bond theories is provided. In addition, research on selected unconventional spectral effects resulting from the substitution of protons with deuterons in hydrogen bridges is presented. The state-of-the-art and originality of the current review are that it presents a combination of uses of FT-IR spectroscopy to explain the way molecules vibrate and the effects of those vibrations on macroscopic properties, hence practical applications of given substances.

Mid-infrared spectroscopy for the rapid quantification of eucalyptus oil adulteration in Australian tea tree oil (Melaleuca alternifolia)

Essential oil distilled from Melaleuca alternifolialeaves, commonly known as tea tree oil, is well known for its biological activity, principally its antimicrobial properties. However, many samples are adulterated with other, cheaper essential oils such as eucalyptus oil. Current methods of detecting such adulteration are costly and time-consuming, making them unsuitable for rapid authentication screening. This study investigated the use of mid-infrared (MIR) spectroscopy for detecting and quantifying the level of eucalyptus oil adulteration in spiked samples of pure Australian tea tree oil. To confirm the authenticity of the tea tree oil samples, GC-MS analysis was used to profile 37 of the main volatile constituents present, demonstrating that the samples conformed to ISO specifications. Three chemometric regression techniques (PLSR, PCR and SVR) were trialled on the MIR spectra, along with a variety of pre-processing techniques. The best-performing full-wavelength PLSR model showed excellent prediction of eucalyptus oil content, with an R²_CV of 0.999 and RMSECV of 1.08 % v/v. The RMSECV could be further improved to 0.82 % v/v through a moving window wavenumber optimisation process. The results suggest that MIR spectroscopy combined with PLSR can be used to predict eucalyptus oil adulteration in Australian tea tree oil samples with a high level of accuracy.

A review of the main methods for composite adsorbents characterization

Adsorption is a promising technology for removing several contaminants from aqueous matrices. In the last years, researchers worldwide have been working on developing composite adsorbents to overcome some limitations and drawbacks of conventional adsorbent materials, which depend on various factors, including the characteristics of the adsorbents. Therefore, it is essential to characterize the composite adsorbents to describe their properties and structure and elucidate the mechanisms, behavior, and phenomenons during the adsorption process. In this sense, this work aimed to review the main methods used for composite adsorbent characterization, providing valuable information on the importance of these techniques in developing new adsorbents. In this paper, we reviewed the following methods: X-Ray diffraction (XRD); spectroscopy; scanning electron microscopy (SEM); N2 adsorption/desorption isotherms (BET and BJH methods); thermogravimetry (TGA); point of zero charge (pHPZC); elemental analysis; proximate analysis; swelling and water retention capacities; desorption and reuse.

Comparative analysis of proximate compositions, mineral and functional chemical groups of 15 different seaweed species

Seaweed is a popular edible source and is associated with many foods and pharmaceutical industries around the world. The current research aims to provide information on the chemical composition of 15 seaweed species, consisted of Chlorophyta, Ochrophyta/Phaeophyceae, and Rhodophyta macroalgae, collected from coastal areas of Sri Lanka. Seaweed samples were subjected to the analysis of lipids, proteins, ash and macro, micro, trace and ultra-trace elements. The highest protein content was recorded in the brown algae. Maximum dietary fiber and ash contents were recorded from green algae. The highest predominant fatty acids were observed from green seaweeds (Caulerpa racemosa); however, linoleic acid (C18:2n6) is the dominant fatty acid of all macroalgae. Mineral contents were highest in the red macroalga; however, copper, zinc and magnesium were also comparatively higher in green alga Ulva lactuca. In conclusion, 15 seaweed species belonging to the three different classes of seaweeds are investigated in details to obtain their biochemical, mineral and fatty acid compositions for the synthesis of novel therapeutic agents. In order to explore biorefinery processes for these seaweeds, as well as how they can potentially be cultivated, more extensive studies are required. Studying and determining the nutritional values of seaweeds will be beneficial with the potentials for future industrial uses and research.

Determination of some edible oils adulteration with paraffin oil using infrared spectroscopy

The spectroscopy of molecular vibrations using mid-infrared or near-infrared techniques was used more and more to characterize different compounds, including edible oil, in order to monitor any changes and to detect fraudulent modifications. This article presents a new method for quantitative adulteration of extra virgin olive oil (EVOO) or corn germ oil (CGO) with a mineral oil, such as paraffin oil (PO). A Fourier transform infrared (FT-IR) spectrometric method, using ATR spectra, was developed for the rapid, direct measurement of edible oils adulteration. The results indicate the efficiency of the proposed method for the detection of paraffin oil in adulteration of EVOO and CGO with RSD (&lt; 3.0%).

Graphical abstract:

Simultaneous Determination of Adulterants in Dietary Food Supplements Using Multivariate Data Analysis after Preconcentration with Novel Nanosorbents and Chromatographic Measurement

Background

The increasing popularity of dietary supplements and, consequently, related adulteration emphasizes the rising need to examine the association of food supplements with fraud. Intentional or unintentional fraud in food supplements by hazardous chemicals compounds is a problem that many countries are struggling with. Much effort have been made to effectively and reliably control the quality of food supplements.

Objective

Due to the importance of the subject, an analytical method for the simultaneous and reliable detection and quantitative determination of three key adulterants in dietary food supplements was developed. The proposed method benefits from analytical methods and multivariate calibration methods to progress the determination of adulterants in a complex matrix.

Methods

HPLC assisted by multivariate curve resolution-alternating least square (MCR-ALS) analysis was used to detect adulterants in real samples after separation and preconcentration using novel mesoporous carbon nanoparticles. Solid-phase extraction (SPE) optimization was accomplished by central composite design (CCD). In order to obtain the best results, the MCR-ALS model was compared with the parallel factor analysis 2 (PARAFAC2) model and validated by estimation of linearity, detection limits, and recovery.

Results

The detection limits and linear dynamics were calculated as 1.5, 4.27, and 4.77 µg/mL, and 1–50, 5–20, and 5–20 µg/mL for caffeine, ephedrine, and fluoxetine, respectively. Mean recovery for determination of caffeine, ephedrine, and fluoxetine using the developed method was reported as 101.75, 91.7, and 92.36, respectively.

Conclusion

The results showed that to avoid negative health outcomes associated with the excessive consumption of adulterated food supplements releasing such products should be carefully regulated. The developed method was validated using statistical factors and showed acceptable and reliable results.

Highlights

(1) The application of MCR-ALS coupled with HPLC-Diode-Array Detection data sets allowed the simultaneous identification and quantification of three key adulterants (caffeine, ephedrine, and fluoxetine) in dietary food supplements. (2) A small amount of the novel adsorbent was successfully used to preconcentrate the trace amounts of adulterants in samples. (3) This method benefits from the chemometrics tools and experimental design to significantly reduce the use of toxic solvents and complicated instruments to propose a less time-consuming method for quantification of multicomponents in the presence of uncalibrated interferents.

The Development of Honey Recognition Models Based on the Association between ATR-IR Spectroscopy and Advanced Statistical Tools

The newly developed prediction models, having the aim to classify Romanian honey samples by associating ATR-FTIR spectral data and the statistical method, PLS-DA, led to reliable differentiations among the samples, in terms of botanical and geographical origin and harvesting year. Based on this approach, 105 out of 109 honey samples were correctly attributed, leading to true positive rates of 95% and 97% accuracy for the harvesting differentiation model. For the botanical origin classification, 83% of the investigated samples were correctly predicted, when four honey varieties were simultaneously discriminated. The geographical assessment was achieved in a percentage of 91% for the Transylvanian samples and 85% of those produced in other regions, with overall accuracy of 88% in the cross-validation procedure. The signals, based on which the best classification models were achieved, allowed the identification of the most significant compounds for each performed discrimination.

A Narrative Review of Recent Advances in Rapid Assessment of Anthocyanins in Agricultural and Food Products

Anthocyanins (ACNs) are plant polyphenols that have received increased attention recently mainly due to their potential health benefits and applications as functional food ingredients. This has also created an interest in the development and validation of several non-destructive techniques of ACN assessments in several food samples. Non-destructive and conventional techniques play an important role in the assessment of ACNs in agricultural and food products. Although conventional methods appear to be more accurate and specific in their analysis, they are also associated with higher costs, the destruction of samples, time-consuming, and require specialized laboratory equipment. In this review article, we present the latest findings relating to the use of several spectroscopic techniques (fluorescence, Raman, Nuclear magnetic resonance spectroscopy, Fourier-transform infrared spectroscopy, and near-infrared spectroscopy), hyperspectral imaging, chemometric-based machine learning, and artificial intelligence applications for assessing the ACN content in agricultural and food products. Furthermore, we also propose technical and future advancements of the established techniques with the need for further developments and technique amalgamations.

Deep Learning and Infrared Spectroscopy: Representation Learning with a β-Variational Autoencoder

Infrared (IR) spectra contain detailed and extensive information about the chemical composition and bonding environment in a sample. However, this information is difficult to extract from complex heterogeneous systems because of overlapping absorptions due to different generative factors. We implement a deep learning approach to study the complex spectroscopic changes that occur in cross-linked polyethylene (PEX-a) pipe by training a β-variational autoencoder (β-VAE) on a database of PEX-a pipe spectra. We show that the β-VAE outperforms principal component analysis (PCA) and learns interpretable and independent representations of the generative factors of variance in the spectra. We apply the β-VAE encoder to a hyperspectrum of a crack in the wall of a pipe to evaluate the spatial distribution of these learned representations. This study shows how deep learning architectures like β-VAE can enhance the analysis of spectroscopic data of complex heterogeneous systems.

Genetic optimization of mid-infrared filters for a machine learning chemical classifier

Miniaturized mid-infrared spectrometers present opportunities for applications that range from health monitoring to agriculture. One approach combines arrays of spectral filters with infrared photodetectors, called filter-array detector-array (FADA) microspectrometers. A paper recently reported a FADA microspectrometer in tandem with machine learning for chemical identification. In that work, a FADA microspectrometer with 20 filters was assembled and tested. The filters were band-pass, or band-stop designs that evenly spanned the microspectrometer's operating wavelength range. However, given that a machine learning classifier can be trained on an arbitrary filter basis, it is not apparent that evenly spaced filters are optimal. Here, through simulations with noise, we use a genetic algorithm to optimize six bandpass filters to best identify liquid and gaseous chemicals. We report that the classifiers trained with the optimized filter sets outperform those trained with evenly spaced filter sets and those handpicked to target the absorption bands of the chemicals investigated.