Mid-infrared (MIR) Spectroscopy for Quality Analysis of Liquid Foods

Non-destructive authentication of Cachaças from Brejo Paraibano based on MIR spectroscopy

Cachaça, an alcoholic beverage derived from the fermentation of sugarcane juice, is a quintessential Brazilian product, with the Brejo Paraibano region standing out as one of the leading producers of high-quality cachaças. Therefore, geographic authentication of these beverages is essential to guarantee their quality and prevent fraud. This study employed mid-infrared spectroscopy (MIR) combined with chemometric models, including One-Class Partial Least Squares (OC-PLS) and Data-Driven Soft Independent Modeling by Class Analogy (DD-SIMCA), to authenticate the geographic origin of cachaças from Brejo Paraibano, as opposed to cachaças originating from other Brazilian regions. The DD-SIMCA model, incorporating spectral preprocessing with baseline shift correction and Savitzky-Golay smoothing using 21 points, achieved 95.6 % sensitivity during training, 100 % in testing, and 100 % specificity, with an overall classification efficiency of 98.4 %. These findings underscore the effectiveness of the proposed approach as a robust, green, and reliable tool to verify the authenticity and geographic origin of cachaças.

Recent advances in photoelectric methods application for cooking oil quality and safety evaluation: a review

Cooking oil is used daily in consumed food and culinary applications; therefore, its safety and quality are very important. Notably, susceptibility to contamination at each processing stage poses threats to living organisms. This review discusses the parameters of oil quality, as well as the role of the various non-destructive photoelectric techniques with respect to its quality and safety, including near-infrared spectroscopy (NIR), mid-infrared spectroscopy, Fourier transform near-infrared spectroscopy, Raman spectroscopy and fluorescence spectroscopy. Data on cooking oil quality, such as values of the following parameters, notably peroxides, thiobarbituric acid, anisidine, iodine, trans-fat and fatty acid profile, carbonyl compounds, adulteration and total polar components, are also demonstrated. Photoelectric methods are rapid and efficient tools for the preliminary screening of cooking oil when aiming to determine its quality before its entry into the food chain. Primarily, NIR has been used to predict most of the cooking oil safety and quality parameters, and thus is considered as the most convenient non-destructive method to be recommended. Accordingly, deep insight into state-of-the-art photoelectric/spectral technologies and the varieties of techniques available provides an opportunity to detect and predict the safety parameters of products prior to their processing and distribution. In this review, we highlight these perspectives with particular emphasis on the cooking oil. © 2025 Society of Chemical Industry.

MIR spectroscopy in ATR mode for Sf9 insect cell line biochemical monitoring in bioprocesses

This work aimed to establish global models with adequate predictive capacity for monitoring nutrients, metabolites, density, and cell viability, based on mid-infrared (MIR) Spectroscopy data in attenuated total reflectance (ATR) mode together with chemometric techniques (Partial Least Squares, PLS; Artificial Neural Network, ANN). To perform biochemical monitoring of the Sf9 insect cell line growth, assays in Schott flask using EX-CELL® 420 with and without critical nutrients supplementation and Sf-900™ III culture serum-free media were performed. The effect of spectral filtering in modeling was also assessed. Global models applied to Sf9 growing in EX-CELL® 420 and Sf-900™ III were confirmed for viable cell density (cells/mL, PLS), cell viability (%, PLS, ANN), glucose (g/L, PLS, ANN), glutamine (g/L, ANN), and glutamate (g/L, PLS, ANN). However, lactate was not accurately quantified with this approach. The mean absolute errors for viable cell density, cell viability, glucose, glutamine, and glutamate global models were 0.90 x 106 cells/mL (PLS), 4.64 % (PLS), 0.42 g/L (PLS), 0.19 g/L (ANN), and 0.09 g/L (PLS), respectively. The errors for the best models were similar to those obtained for other animal cell lines and spectroscopic techniques. The best-defined chemometrics global models enable the reduction of analytical time and cost during bioprocess development using the Sf9 cell line and EX-CELL® 420 and Sf-900TM III culture media.

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.

Infrared Evanescent Wave Sensing Based on a Ge10As30Se40Te20 Fiber for Alcohol Detection

Infrared evanescent wave sensing based on chalcogenide fiber is an emerging technology for qualitative and quantitative analysis of most organic compounds. Here, a tapered fiber sensor made from Ge₁₀As₃₀Se₄₀Te₂₀ glass fiber was reported. The fundamental modes and intensity of evanescent waves in fibers with different diameters were simulated with COMSOL. The 30 mm length tapered fiber sensors with different waist diameters, 110, 63, and 31 μm, were fabricated for ethanol detection. The sensor with a waist diameter of 31 μm has the highest sensitivity of 0.73 a.u./% and a limit of detection (LoD) of 0.195 vol.% for ethanol. Finally, this sensor has been used to analyze alcohols, including Chinese baijiu (Chinese distilled spirits), red wine, Shaoxing wine (Chinese rice wine), Rio cocktail, and Tsingtao beer. It is shown that the ethanol concentration is consistent with the nominal alcoholicity. Moreover, other components such as CO₂ and maltose can be detected in Tsingtao beer, demonstrating the feasibility of its application in detecting food additives.

Design of MIR Dispersive Spectrograph System with Uncooled Microbolometer

To make the mid-infrared (MIR) dispersive spectrograph a practical tool in industrial food processing lines, we designed a dispersive spectrograph system with an uncooled microbolometer focal plane array (FPA) detector for MIR spectral acquisition. To precisely regulate the angle of a rotatable grating to acquire the MIR spectrum, the spectral resolution and spatial resolution of the system were rigorously controlled to improve system performance. In the reflectance operation mode of the MIR dispersive spectrograph, the uncooled microbolometer FPA detector offered a maximum spectral resolution of 12 nm for the MIR, when a 300 grooves/mm blazed grating was used. Utilizing an optical parametric oscillator (OPO) pulse laser source, the wavelengths of the first-order diffraction were validated, and the system's spectral resolution limit was determined. As a line-scanning source, a Globar broadband source was installed, and the USAF 1951 Resolution Calculator was used to establish the spatial resolution of the imaging spectrograph. Using NI LabView, the logical operational technique for controlling the MIR dispersive spectrograph was encoded into system firmware. The GUI and test results are thoroughly described.

Application on infrared spectroscopy for the analysis of total phenolic compounds in fruits

Recent studies have demonstrated the metabolic benefits of phenolic compounds on human health. However, traditional analytical methods used for quantification of total phenolic compounds are time-consuming, laborious, require a high volume of reagents, mostly toxic substances, and involve several steps that can result in systematic and instrumental errors. Spectroscopic techniques have been used as alternatives to these methods for the determination of bioactive compounds directly in the food matrix by minimal sample preparation, without using toxic reagents. Therefore, this overview presents the advantages of nondestructive methods focusing on infrared spectroscopy (IR), for the quantification of total phenolic compounds in fruits. In addition, the main difficulties in applying these spectroscopic techniques are presented, as well as a comparison between the quantification of total phenolic compounds by traditional and IR methods. This review concludes by focusing on model building, highlighting that IR data are mainly processed using the partial least-squares (PLS) regression method to predict total phenolic content. The development of portable and inexpensive IR instruments, combined with multivariate data processing, could give to the consumers a straightforward technology to evaluate the total phenolic content of fruits prior to purchase.

Honey Discrimination Using Fourier Transform-Infrared Spectroscopy

Infrared spectroscopy is a widely used method of analysis to monitor various characteristics in the honey products analysis, to highlight these changes and to detect fraudulent modifications. In this way honey products could not be avoided. This article reviews some of the most important applications of these spectroscopic procedures in order to discriminate different types of honey and other products published between 2015–2022.

Non-destructive assessment of vitamin C in foods: a review of the main findings and limitations of vibrational spectroscopic techniques

The constant increase in the demand for safe and high-quality food has generated the need to develop efficient methods to evaluate food composition, vitamin C being one of the main quality indicators. However, its heterogeneity and susceptibility to degradation makes the analysis of vitamin C difficult by conventional techniques, but as a result of technological advances, vibrational spectroscopy techniques have been developed that are more efficient, economical, fast, and non-destructive. This review focuses on main findings on the evaluation of vitamin C in foods by using vibrational spectroscopic techniques. First, the fundamentals of ultraviolet–visible, infrared and Raman spectroscopy are detailed. Also, chemometric methods, whose use is essential for a correct processing and evaluation of the spectral information, are described. The use and importance of vibrational spectroscopy in the evaluation of vitamin C through qualitative characterization and quantitative analysis is reported. Finally, some limitations of the techniques and potential solutions are described, as well as future trends related to the utilization of vibrational spectroscopic techniques.

Advanced process analytical tools for identification of adulterants in edible oils - A review

This review summarizes the use of spectroscopic processes-based analytical tools coupled with chemometric techniques for the identification of adulterants in edible oil. Investigational approaches of process analytical tools such asspectroscopy techniques, nuclear magnetic resonance (NMR), hyperspectral imaging (HSI), e-tongue and e-nose combined with chemometrics were used to monitor quality of edible oils. Owing to the variety and intricacy of edible oil properties along with the alterations in attributes of the PAT tools, the reliability of the tool used and the operating factors are the crucial components which require attention to enhance the efficiency in identification of adulterants. The combination of process analytical tools with chemometrics offers a robust technique with immense chemotaxonomic potential. These involves identification of adulterants, quality control, geographical origin evaluation, process evaluation, and product categorization.

High sensitivity infrared spectroscopy with a diamond waveguide on aluminium nitride

Mid-infrared waveguide spectroscopy promises highly sensitive detection and characterization of organic molecules. Different material combinations for waveguides and cladding have been demonstrated with promising results, each with its own strengths and weaknesses in terms of sensitivity, transmission window and robustness. In this article we present a 5 μm thick diamond planar waveguide on aluminium nitride cladding, using a new fabrication and polishing method. Diamond has a very wide transmission window in the infrared, and its hardness and high chemical stability allows for chemistries and cleaning protocols that may damage other materials. With an aluminium nitride cladding the waveguide has a useable range between 1000 and 1900 cm^-1, which we demonstrate using a tunable quantum cascade laser (QCL). This is a large improvement over silicon dioxide cladding. Compared to previously demonstrated free-standing diamond waveguides, the robustness of the sensor is greatly improved, which allows for a thinner diamond layer and increased sensitivity. The new waveguide was used in a QCL-based optical setup to detect acetone in deuterium oxide and isopropyl alcohol in water. The measurements showed higher sensitivity and lower noise level than previous demonstrations of mid-infrared diamond waveguides, resulting in a two orders of magnitude lower detectable concentration.

Discrimination of soils managed with different sources of fertilization and plant species in organic and conventional farming through near-infrared spectroscopy and chemometrics

BACKGROUND

The combination of near-infrared (NIR) spectroscopy and chemometrics can be used to group or discriminate soils based on spectral response. In this study, we conducted discrimination and classification analyses on soils managed with different sources of fertilization and plant species grown in organic and conventional farming systems.

RESULTS

Principal component analysis explained 96% (PC1) and 3% (PC2) of the data variability and separated the soil samples of organic and conventional management systems. The wavenumbers that contributed most to the separation of the management systems were in the range of 3600 and 7300 cm^-1 , especially the absorption peaks of 3700 and 4600 cm^-1 (characteristic of CH and NH combinations), and 5200 and 7000 cm^-1 (typical of OH combinations). Machine learning analysis using k-nearest neighbor and random forest algorithms was efficient in classifying soil samples according to management system with an accuracy of 97.8% and can therefore be used for future classification studies.

CONCLUSION

Based on the results, we strongly recommend the use of NIR spectroscopy associated with chemometrics for discriminating soils grown with Malus domestica, Musa spp., Oryza sativa and Solanum tuberosum L. under organic and conventional management systems through spectral response. © 2021 Society of Chemical Industry.

Diamond-Coated Silicon ATR Elements for Process Analytics

Infrared attenuated total reflection (ATR) spectroscopy is a common laboratory technique for the analysis of highly absorbing liquids or solid samples. However, ATR spectroscopy is rarely found in industrial processes, where inline measurement, continuous operation, and minimal maintenance are important issues. Most materials for mid-infrared (MIR) spectroscopy and specifically for ATR elements do not have either high enough infrared transmission or sufficient mechanical and chemical stability to be exposed to process fluids, abrasive components, and aggressive cleaning agents. Sapphire is the usual choice for infrared wavelengths below 5 µm, and beyond that, only diamond is an established material. The use of diamond coatings on other ATR materials such as silicon will increase the stability of the sensor and will enable the use of larger ATR elements with increased sensitivity at lower cost for wavelengths above 5 µm. Theoretical and experimental investigations of the dependence of ATR absorbances on the incidence angle and thickness of nanocrystalline diamond (NCD) coatings on silicon were performed. By optimizing the coating thickness, a substantial amplification of the ATR absorbance can be achieved compared to an uncoated silicon element. Using a compact FTIR instrument, ATR spectra of water, acetonitrile, and propylene carbonate were measured with planar ATR elements made of coated and uncoated silicon. Compared to sapphire, the long wavelength extreme of the spectral range is extended to approximately 8 μm. With effectively nine ATR reflections, the sensitivity is expected to exceed the performance of typical diamond tip probes.

ATR-FTIR-MIR Spectrometry and Pattern Recognition of Bioactive Volatiles in Oily versus Microencapsulated Food Supplements: Authenticity, Quality, and Stability

Fourier transform infrared spectroscopy on the middle infrared region (ATR-FTIR-MIR) proved to be a convenient and reliable technique to evaluate foods' quality and authenticity. Plants' essential oils are bioactive mixtures used as such or in different oily or microencapsulated formulations, beneficial to human health. Six essential oils (thyme, oregano, juniperus, tea tree, clove, and cinnamon) were introduced in three oily formulations (Biomicin, Biomicin Forte, and Biomicin urinary) and these formulations were microencapsulated on fructose and maltodextrin matrices. To study their stability, the microencapsulated powders were kept under light irradiation for 14 days at 25 °C or introduced in biopolymer capsules. All variants were analysed by ATR-FTIR-MIR, recording wavenumbers and peak intensities (3600-650 cm^-1). The data were processed by Unscrambler and Metaboanalyst software, with specific algorithms (PCA, PLSDA, heatmaps, and random forest analysis). The results demonstrated that ATR-FTIR-MIR can be successfully applied for fingerprinting and finding essential oil biomarkers as well as to recognize this pattern in final microencapsulated food supplements. This study offers an improved ATR-FTIR-MIR procedure coupled with an adequate chemometric analysis and accurate data interpretation, to be applied for the evaluation of authenticity, quality, traceability, and stability during storage of essential oils incorporated in different matrices.

Attenuated Total Reflectance-Fourier Transform Infrared Spectroscopy (ATR-FTIR) Coupled with Chemometrics, to Control the Botanical Authenticity and Quality of Cold-Pressed Functional Oils Commercialized in Romania

Attenuated total reflectance-Fourier transform infrared ppectroscopy (ATR-FTIR) proved to be a reliable, rapid, and easy-to-use technique to evaluate vegetable oils quality and authenticity. The spectral range of the middle infrared region (MIR) of FTIR spectra, from 4000 to 600 cm−1, has been commonly used to fingerprint specific functional groups of lipids and their modified forms induced by oxidation of thermal treatment. The applicability of FTIR-MIR spectroscopy in assessing oil fingerprinting and quality parameters is crucially dependent on the chemometric methods, including calibrations with authentic samples. We report here the evaluation of seven types of cold-pressed functional oils (sunflower, pumpkin, hempseed, soybean, walnut, linseed, sea buckthorn) produced in Romania, provided directly from small enterprises (as genuine, process-controlled authentic samples) comparative to commercialized samples. Concomitantly, olive oils of similar claimed quality were investigated. The ATR-FTIR-MIR data were complemented by UV–Vis spectral fingerprints and multivariate analysis using Unscrambler X.10.4 and Metaboanalyst 4.0 software (e.g., PCA, PLSDA, cluster analysis, heatmap, Random forest analysis) and ANOVA post-hoc analysis using Fischer’s least significant difference. The integration of spectral and chemometric analysis proved to offer valuable criteria for their botanical group recognition, individual authenticity, and quality, easy to be applied for large cohorts of commercialized oils.

Hyperspectral imaging and improved feature variable selection for automated determination of deoxynivalenol in various genetic lines of barley kernels for resistance screening

Fusarium head blight (FHB), a fungus disease of small grain cereal crops, results in reduced yields and diminished value of harvested grain due to the presence of deoxynivalenol (DON), a mycotoxin produced by the causal pathogen Fusarium graminearum. DON and other tricothecene mycotoxins pose serious health risks to both humans and livestock, especially swine. Due to these health concerns, barley used for malting, food or feed is routinely assayed for DON levels. Various methods are available for assaying DON levels in grain samples including enzyme-linked immunosorbent assay (ELISA) and gas chromatography-mass spectrometry (GC-MS). ELISA and GC-MS are very accurate; however, assaying grain samples by these techniques are laborious, expensive and destructive. In this study, we explored the feasibility of using hyperspectral imaging (382-1030 nm) to develop a rapid and non-destructive protocol for assaying DON in barley kernels. Samples of 888 and 116 from various genetic lines were selected for calibration and prediction. Full-wavelength locally weighted partial least squares regression (LWPLSR) achieved high accuracy with the coefficient of determination in prediction (R²_P) of 0.728 and root mean square error of prediction (RMSEP) of 3.802. Competitive adaptive reweighted sampling (CARS) was used to choose potential feature wavelengths, and these selected variables were further optimized using the iterative selection of successive projections algorithm (ISSPA). The CARS-ISSPA-LWPLSR model developed using 7 feature variables yielded R²_P of 0.680 and RMSEP of 4.213 in DON content prediction. Based on the 7 wavelengths selected by CARS-ISSPA, partial least square discriminant analysis (PLSDA) discriminated barley kernels having lower DON (less than1.25 mg/kg) levels from those with higher levels (including 1.25-3 mg/kg, 3-5 mg/kg, and 5-10 mg/kg), with Matthews correlation coefficient in cross-validation (M-R_CV) of as high as 0.931. The results demonstrate that hyperspectral imaging have potential for accelerating non-destructive DON assays of barley samples.

Use of elemental profiles to verify geographical origin and botanical variety of Spanish honeys with a protected denomination of origin

Honey with Protected Denomination of Origin (PDO) could be an attractive target for fraudsters. Elemental profiles by Energy Dispersive-X Ray Fluorescence were processed by multivariate methods to classify 183 PDO honeys produced in three regions of Spain (Liébana, Granada, Tenerife). Additional honey samples (18) produced in a fourth region without PDO (El Bierzo) separated well from the PDO clusters. The manganese content was a discriminant marker of Liébana PDO and El Bierzo, that could also be differentiated from each other. Within each region, distinct clusters revealed differences between dark vs light varieties, multi- vs uni-floral honey and producers of the same PDO. The developed models were validated with 131 samples produced outside the PDO regions and El Bierzo. The proposed classification approach could be implemented as a fast screening tool to support pollen analysis in honey authentication. The reduced number of observations in some light honey models affected their performance.

Use of Spectroscopic Techniques to Monitor Changes in Food Quality during Application of Natural Preservatives: A Review

Consumer demand for food of high quality has driven research for alternative methods of food preservation on the one hand, and the development of new and rapid quality assessment techniques on the other hand. Recently, there has been a growing need and interest in healthier food products, which has led to an increased interest in natural preservatives, such as essential oils, plant extracts, and edible films and coatings. Several studies have shown the potential of using biopreservation, natural antimicrobials, and antioxidant agents in place of other processing and preservation techniques (e.g., thermal and non-thermal treatments, freezing, or synthetic chemicals). Changes in food quality induced by the application of natural preservatives have been commonly evaluated using a range of traditional methods, including microbiology, sensory, and physicochemical measurements. Several spectroscopic techniques have been proposed as promising alternatives to the traditional time-consuming and destructive methods. This review will provide an overview of recent studies and highlight the potential of spectroscopic techniques to evaluate quality changes in food products following the application of natural preservatives.

Advanced Machine Learning in Point Spectroscopy, RGB- and Hyperspectral-Imaging for Automatic Discriminations of Crops and Weeds: A Review

Crop productivity is readily reduced by competition from weeds. It is particularly important to control weeds early to prevent yield losses. Limited herbicide choices and increasing costs of weed management are threatening the profitability of crops. Smart agriculture can use intelligent technology to accurately measure the distribution of weeds in the field and perform weed control tasks in selected areas, which cannot only improve the effectiveness of pesticides, but also increase the economic benefits of agricultural products. The most important thing for an automatic system to remove weeds within crop rows is to utilize reliable sensing technology to achieve accurate differentiation of weeds and crops at specific locations in the field. In recent years, there have been many significant achievements involving the differentiation of crops and weeds. These studies are related to the development of rapid and non-destructive sensors, as well as the analysis methods for the data obtained. This paper presents a review of the use of three sensing methods including spectroscopy, color imaging, and hyperspectral imaging in the discrimination of crops and weeds. Several algorithms of machine learning have been employed for data analysis such as convolutional neural network (CNN), artificial neural network (ANN), and support vector machine (SVM). Successful applications include the weed detection in grain crops (such as maize, wheat, and soybean), vegetable crops (such as tomato, lettuce, and radish), and fiber crops (such as cotton) with unsupervised or supervised learning. This review gives a brief introduction into proposed sensing and machine learning methods, then provides an overview of instructive examples of these techniques for weed/crop discrimination. The discussion describes the recent progress made in the development of automated technology for accurate plant identification as well as the challenges and future prospects. It is believed that this review is of great significance to those who study automatic plant care in crops using intelligent technology.

Cylindrical IR-ATR Sensors for Process Analytics

Infrared attenuated total reflection (ATR) spectroscopy is a common laboratory technique for the analysis of highly absorbing liquids and solids. However, in a process environment, maintaining a sufficient sample exchange and cleaning of the sensitive surface of the element is a crucial issue. An important industrial application is the measurement of isocyanate concentrations. Isocyanates are necessary for the fabrication of polyurethane materials and are among the chemicals with the highest production volume worldwide. For process applications, narrowband photometers or MEMS spectrometers are more appropriate than the use of bulky FTIR instruments frequently encountered in a laboratory environment. Toluene diisocyanate (TDI) and hexamethylene diisocyanate (HDI) concentrations are measured with a planar ATR photometer setup. Using a miniature Fabry–Perot interferometer (FPI), trace concentrations below 100 ppm (m/m) are detected. By employing an ATR element of the cylindrical shape, sensors can be realized with a smooth surface ideally suited for an automatic cleaning system in a process environment. A laboratory setup with sapphire tubes as ATR elements for incorporation in a liquid flow system is described. Reflection and transmission configurations were investigated. Measurements with acetonitrile as a less toxic substitute showed that with cylindrical ATR sensors’ detection limits for isocyanate concentrations below 100 ppm (m/m) are feasible.