Rapid and non-destructive approach for the detection of fried mustard oil adulteration in pure mustard oil via ATR-FTIR spectroscopy-chemometrics

Nondestructive and robust detection of different kinds of vegetable oils adulterated with used frying oil by multi-optical properties

Developing a rapid and non-invasive detection tool for frying oil adulteration in pure vegetable oils holds the considerable significance. In this study, a self-developed optical system which combined double integrating spheres (DIS) and laser induced fluorescence (LIF) techniques was applied to obtain the optical properties of adulterated oils, which involved four categories: peanut, corn, rapeseed, and soybean oils, with each category from two distinct brands. The captured multi-optical properties included absorption coefficient (μa), reduced scattering coefficient (μ's) and fluorescence intensity (F). The relationships between optical properties and the primary chemical attributes in oils were explored. And a "two-step" modelling method was proposed for estimating the adulteration ratios of frying oil, which involved initial clustering for eight groups of vegetable oils by principal component analysis (PCA). Subsequently, six quantitative prediction models were developed using multiple linear regressions (MLR) based on clustering outcomes. Furthermore, least squares support vector regression (LSSVR) models were calibrated based on F at 475 nm and fluorescence correction parameters (μa at 375 and 475 nm), to predict the peroxide values (PV) and acid values (AV) of oils. The results demonstrated evident distinctions in the F and μa spectra among the four types of vegetable oils. Complex matrix interference and fluctuating absorption capacities precluded the detection of a consistent pattern between chemical composition and spectral data. The proposed "two-step" modelling method showed good predictive performance for adulteration ratio, with determination coefficient for prediction set (r2p) of 0.851, root mean square errors for prediction set (RMSEP) of 5.204 %, and relative percent difference (RPD) of 2.540. LSSVR models exhibited excellent predictive performance for PV (r2p = 0.925, RMSEP = 0.200, RPD = 3.490) and AV (r2p = 0.996, RMSEP = 0.011, RPD = 16.740). The overall results of this study indicated the feasibility of utilising multi-optical properties for the rapid, non-invasive and accurate detection of adulteration of frying oils.

An Exploration of the Biochemistry of Mustard Seed Meals: A Phytochemical and In Silico Perspective

The present investigation deals with comparisons drawn among three types of different mustard seed coat colors, namely, Black (Brassica nigra), Brown (Brassica juncea), and White (Sinapis alba), with respect to protein's bio-availability through pepsin digestibility, with and without the involvement of major anti-nutritional factors (glucosinolate type AITC, Allylisothiothiocyanate) and relative food functions. These are validated by means of crude protein determination, precipitated protein isolate preparation for evaluating the fat absorption capacity (FAC), emulsifying activity (EA), emulsion stability (ES), whippability, foam stability (FS), the nitrogen solubility index (NSI), and the protein dispersibility index (PDI). The results indicate that the partial removal of glucosinolates from brown mustard (0.91 to 0.31%), black mustard (0.74 to 0.31%), and white mustard (0.58 to 0.30%) improved protein's digestibility, as analyzed through a pepsin assay, with values of 12.84, 12.60, and 4.53% in brown, black, and white mustard, respectively. Among functional properties, the highest FAC, whippability, foam stability, and NSI values were noted in the brown mustard seed meal, derived from precipitated protein isolates, while EA and PDI were the highest in white mustard seeds, and black seeds possessed the highest ES value. Interestingly, these mustard seed meals are limited in their consumption, albeit by virtue of the different phytochemical and food functional parameters that are being elucidated here. The present research shows the relevance of different food functional properties and the presence of anti-nutritional factors, and uses protein-digestibility tests, which are important deciding parameters for enhanced food consumption in animal diets. Additionally, targeted molecular and protein-protein docking results revealed how and why the mustard seed meals are limited in their consumption by virtue of various metabolite interactions. This thereby opens the gateways to many required in vivo and in silico future research insights among AITC-pepsin, AITC-myrosinase, pepsin-myrosinase, and cruciferin-myrosinase complexes. Consequently, the metabolic pathways governing AITC involved in the glucosinolate-myrosinase system need to be studied in depth for a better understanding of in vivo AITC metabolism. This knowledge can guide future studies in improving the health benefits of mustard seeds and seed meals while addressing their consumption limitations.

Adulteration in edible oil (mustard oil) and ghee; detection and their effects on human health

Edible oils and ghee are vital parts of our daily culinary practices. In recent years, owing to heightened demand in the domestic and global markets, consistent reports regarding the adulteration of edible oils and ghee with substandard ingredients have been reported. Adulteration in edible oils is widespread, with distinctive contaminants, including cottonseed, mineral, and lower-cost oils like palm olein. In the case of ghee, it is repeatedly combined with animal fats, synthetic materials, or vanaspati. The consumption of contaminated oils and trans-fats within the human diet has resulted in adverse health effects, including cardiovascular diseases, digestive disorders, and even cancer. The review aims to summarize various adulterants found in edible oil (mustard oil) and ghee, their detection techniques, and harmful effects. This review provides an overview of the contemplation linked to the adulteration of edible oils and ghee, the compliance with relevant regulations, and the technological approaches available for detection. The detection technologies for identifying adulteration in edible oils are chromatography and spectroscopy biochemical methods and the use of high-precision analytical instruments. The presence of adulterants in edible oil and ghee undermines our societal integrity and our ethical standards. Awareness among consumers is essential for effectively combating these adulterations.

Physicochemical characterization of antioxidant film based on ternary blend of chitosan and Tulsi-Ajwain essential oil for preserving walnut

This study focuses on changes in the physiochemical properties of chitosan film when incorporated with a blend of essential oils of Tulsi and Ajwain. The essential oil blend-loaded films showed a decrement in transparency. Tulsi essential oil decreased the moisture content, swelling capacity, and water solubility. However, adding Ajwain along with Tulsi essential oil led to a significant increase in these properties. Meanwhile, the water vapor transmission rate didn't change significantly due to non-polar constituents in Tulsi essential oil, except when only Ajwain essential oil was present. The mechanical properties showed that the tensile strength of films increased with the addition of Tulsi essential oil (14.95 MPa to 31.27 MPa) but decreased further with increasing Ajwain oil concentration in films (32.13 MPa to 15.89 MPa). On the other hand, an increment in percent elongation at break (8.26 % to 24.02 %) was observed due to the excellent plasticization effect of Ajwain essential oil. Antioxidant activity was observed for the Tulsi essential oil-containing films and increased significantly with adding Ajwain essential oil. Finally, walnuts were packed in the active film. The active film showed better antioxidant activity against the oxidation of oil in walnuts, which the FTIR of the packed product confirmed.

Trends in authentication of edible oils using vibrational spectroscopic techniques

The authentication of edible oils has become increasingly important for ensuring product quality, safety, and compliance with regulatory standards. Some prevalent authenticity issues found in edible oils include blending expensive oils with cheaper substitutes or lower-grade oils, incorrect labeling regarding the oil's source or type, and falsely stating the oil's origin. Vibrational spectroscopy techniques, such as infrared (IR) and Raman spectroscopy, have emerged as effective tools for rapidly and non-destructively analyzing edible oils. This review paper offers a comprehensive overview of recent advancements in using vibrational spectroscopy for authenticating edible oils. The fundamental principles underlying vibrational spectroscopy are introduced and chemometric approaches that enhance the accuracy and reliability of edible oil authentication are summarized. Recent research trends highlighted in the review include authenticating newly introduced oils, identifying oils based on their specific origins, adopting handheld/portable spectrometers and hyperspectral imaging, and integrating modern data handling techniques into the use of vibrational spectroscopic techniques for edible oil authentication. Overall, this review provides insights into the current state-of-the-art techniques and prospects for utilizing vibrational spectroscopy in the authentication of edible oils, thereby facilitating quality control and consumer protection in the food industry.

Feasibility of Using a Cheap Colour Sensor to Detect Blends of Vegetable Oils in Avocado Oil

This proof-of-concept study explored the use of an RGB colour sensor to identify different blends of vegetable oils in avocado oil. The main aim of this work was to distinguish avocado oil from its blends with canola, sunflower, corn, olive, and soybean oils. The study involved RGB measurements conducted using two different light sources: UV (395 nm) and white light. Classification methods, such as Linear Discriminant Analysis (LDA) and Least Squares Support Vector Machine (LS-SVM), were employed for detecting the blends. The LS-SVM model exhibited superior classification performance under white light, with an accuracy exceeding 90%, thus demonstrating a robust prediction capability without evidence of random adjustments. A quantitative approach was followed as well, employing Multiple Linear Regression (MLR) and LS-SVM, for the quantification of each vegetable oil in the blends. The LS-SVM model consistently achieved good performance (R2 > 0.9) in all examined cases, both for internal and external validation. Additionally, under white light, LS-SVM models yielded root mean square errors (RMSE) between 1.17-3.07%, indicating a high accuracy in blend prediction. The method proved to be rapid and cost-effective, without the necessity of any sample pretreatment. These findings highlight the feasibility of a cost-effective colour sensor in identifying avocado oil blended with other oils, such as canola, sunflower, corn, olive, and soybean oils, suggesting its potential as a low-cost and efficient alternative for on-site oil analysis.

Comparative study of conventional frying and air frying on the quality of potatoes (Solanum tuberosum L.)

The human being has historically consumed fried foods for centuries; however, conventional frying has a disadvantage, immersion in vegetable and/or animal oils, which leads to the search for different options. This is why air frying is a good alternative, which still has a wide field of study. In this work, frozen French fries of a brand marketed in Mexico that were subjected to frying in canola oil and air frying were compared. They were evaluated through the change in the removed moisture content, water activity, color profile, hardness, fracturability, and surface damage by SEM, thermal analysis by MDSC, and chemical by FTIR-ATR spectroscopy. Air-fried French fries were found to contain about 48% less moisture, fewer perceptible color changes, and less surface damage translated into better crunchiness compared with conventionally fried. It was also found that the changes at the chemical level are smaller, mainly attributed to the absence of canola oil and that the thermal transitions are more stable in terms of temperatures and enthalpies, which makes it possible to emphasize that air frying is a good alternative for developing new fried products that allow expanding the variety of these in the market without sacrificing some quality attributes.

A methodological approach to preprocessing FTIR spectra of adulterated sesame oil

Fourier transform infrared (FTIR) spectroscopy is established as an effective and fast method for the confirmation of the authenticity of food and among other, edible oils. However, no standard procedure is available for applying preprocessing as a vital step in obtaining accurate results from spectra. This study proposes a methodological approach to preprocessing FTIR spectra of sesame oil adulterated with vegetable oils (canola oil, corn oil, and sunflower oil). The primary preprocessing methods investigated are orthogonal signal correction (OSC), standard normal variate transformation (SNV), and extended multiplicative scatter correction (EMSC). Other preprocessing methods are used both as standalone methods and in combination with the primary preprocessing methods. The preprocessing results are compared using partial least squares regression (PLSR). OSC alone or with detrending were the most accurate in predicting the adulteration level of sesame oil, with a maximum coefficient of prediction (R²_p) range of 0.910 to 0.971 for different adulterants.

Detection and Quantification of Adulteration in Krill Oil with Raman and Infrared Spectroscopic Methods

Raman and infrared spectroscopy, used as individual and low-level fused datasets, were evaluated to identify and quantify the presence of adulterants (palm oil, PO; ω-3 concentrates in ethyl ester, O3C and fish oil, FO) in krill oil. These datasets were qualitatively analysed with principal component analysis (PCA) and classified as adulterated or unadulterated using support vector machines (SVM). Using partial least squares regression (PLSR), it was possible to identify and quantify the adulterant present in the KO mixture. Raman spectroscopy performed better (r² = 0.98; RMSEP = 2.3%) than IR spectroscopy (r² = 0.91; RMSEP = 4.2%) for quantification of O3C in KO. A data fusion approach further improved the analysis with model performance for quantification of PO (r² = 0.98; RMSEP = 2.7%) and FO (r² = 0.76; RMSEP = 9.1%). This study demonstrates the potential use of Raman and IR spectroscopy to quantify adulterants present in KO.

A New Chemometrically Assisted UV Spectrophotometric Method for Simultaneous Determination of Tamsulosin and Dutasteride in Their Pharmaceutical Mixture

BACKGROUND

Tamsulosin (TAM) and dutasteride (DUT) are ranked among the most frequently prescribed therapies in urology. Interestingly, studies have also been carried out on TAM/DUT in terms of their ability to protect against recent COVID-19. However, very few studies were reported for their simultaneous quantification in their combined dosage form and were mainly based on chromatographic analysis. Subsequently, it is very important to offer a simple, selective, sensitive, and rapid method for the quantification of TAM and DUT in their challenging dosage form.

OBJECTIVE

In this study, a new chemometrically assisted ultraviolet (UV) spectrophotometric method has been presented for the quantification of TAM and DUT without any prior separation.

METHOD

For the calibration set, a partial factorial experimental design was used, resulting in 25 mixtures with central levels of 20 and 25 μg/mL for TAM and DUT, respectively. In addition, to assess the predictive ability of the developed approaches, another central composite design of 13 samples was used as a validation set. Post-processing by chemometric analysis of the recorded zero-order UV spectra of these sets has been applied. These chemometric approaches include partial least-squares (PLS) and genetic algorithm (GA), as an effective variable selection technique, coupled with PLS.

RESULTS

The models' validation criteria displayed excellent recoveries and lower errors of prediction.

CONCLUSIONS

The proposed models were effectively used to determine TAM/DUT in their combined dosage form, and statistical comparison with the reported method revealed satisfactory results.

HIGHLIGHTS

Overall, this work presents powerful simple, selective, sensitive, and precise methods for simultaneous quantification of TAM/DUT in their dosage form with satisfactory results. The predictive ability and accuracy of the developed methods offer the opportunity to be employed as a quality control technique for the routine analysis of TAM/DUT when chromatographic instruments are not available.

A Review of Advanced Methods for the Quantitative Analysis of Single Component Oil in Edible Oil Blends

Edible oil blends are composed of two or more edible oils in varying proportions, which can ensure nutritional balance compared to oils comprising a single component oil. In view of their economical and nutritional benefits, quantitative analysis of the component oils in edible oil blends is necessary to ensure the rights and interests of consumers and maintain fairness in the edible oil market. Chemometrics combined with modern analytical instruments has become a main analytical technology for the quantitative analysis of edible oil blends. This review summarizes the different oil blend design methods, instrumental techniques and chemometric methods for conducting single component oil quantification in edible oil blends. The aim is to classify and compare the existing analytical techniques to highlight suitable and promising determination methods in this field.

Reflectance Spectroscopy with Multivariate Methods for Non-Destructive Discrimination of Edible Oil Adulteration

The visible and near-infrared (Vis-NIR) reflectance spectroscopy was utilized for the rapid and nondestructive discrimination of edible oil adulteration. In total, 110 samples of sesame oil and rapeseed oil adulterated with soybean oil in different levels were produced to obtain the reflectance spectra of 350–2500 nm. A set of multivariant methods was applied to identify adulteration types and adulteration rates. In the qualitative analysis of adulteration type, the support vector machine (SVM) method yielded high overall accuracy with multiple spectra pretreatments. In the quantitative analysis of adulteration rate, the random forest (RF) combined with multivariate scattering correction (MSC) achieved the highest identification accuracy of adulteration rate with the full wavelengths of Vis-NIR spectra. The effective wavelengths of the Vis-NIR spectra were screened to improve the robustness of the multivariant methods. The analysis results suggested that the competitive adaptive reweighted sampling (CARS) was helpful for removing the redundant information from the spectral data and improving the prediction accuracy. The PLSR + MSC + CARS model achieved the best prediction performance in the two adulteration cases of sesame oil and rapeseed oil. The coefficient of determination (RPcv2) and the root mean square error (RMSEPcv) of the prediction set were 0.99656 and 0.01832 in sesame oil adulterated with soybean oil, and the RPcv2 and RMSEPcv were 0.99675 and 0.01685 in rapeseed oil adulterated with soybean oil, respectively. The Vis-NIR reflectance spectroscopy with the assistance of multivariant analysis can effectively discriminate the different adulteration rates of edible oils.

Identification of cumin and fennel from different regions based on generative adversarial networks and near infrared spectroscopy

Cumin (Cuminum cyminum) and fennel (Foeniculum vulgare) are widely used seasonings and play a very important role in industries such as breeding, cosmetics, winemaking, drug discovery, and nano-synthetic materials. However, studies have shown that cumin and fennel from different regions not only differ greatly in the content of lipids, phenols and proteins but also the substances contained in their essential oils are also different. Therefore, realizing precise identification of cumin and fennel from different regions will greatly help in quality control, market fraud and production industrialization. In this experiment, cumin and fennel samples were collected from each region, a total of 480 NIR spectra were collected. We used deep learning and traditional machine learning algorithms combined with near infrared (NIR) spectroscopy to identify their origin. To obtain the model with the best generalization performance and classification accuracy, we used principal component analysis (PCA) to reduce spectral data dimensionality after Rubberband baseline correction, and then established classification models including quadratic discriminant analysis based on PCA (PCA-QDA) and multilayer perceptron based on PCA (PCA-MLP). We also directly input the spectral data after baseline correction into convolutional neural networks (CNN) and generative adversarial networks (GAN). The experimental results show that GAN is more accurate than the PCA-QDA, PCA-MLP and CNN models, and the classification accuracy reached 100%. In the cumin and fennel classification experiment in the same region, the four models achieve great classification results from three regions under the condition that all model parameters remain unchanged. The experimental results show that when the training data are limited and the dimension is high, the model obtained by GAN using competitive learning has more generalization ability and higher classification accuracy. It also provides a new method for solving the problem of limited training data in food research and medical diagnosis in the future.

Monitoring of frying process in canola oil blend using fourier transform infrared and chemometrics techniques

The production of trans-fats and chemical changes during the process of frying are serious public health concerns and must be monitored efficiently. For this purpose, the canola oil was formulated with different ratio of extra virgin olive oil and palm olein using D-optimal mixture design, and the best formulation (67:22:11) based on free fatty acid (FFA) content, peroxide value (PV), and iodine value (IV) as responses was selected for multiple frying process. The data on FFA, PV, and IV along with Fourier transform-infrared (FT-IR) spectra were taken after each frying up to ten frying. The spectral data were preprocessed with standard normal variate followed by principal component analysis which is clearly showing the differentiation for various frying. Similarly, partial least square regression was applied to predict the FFA (0.37%-1.63%), PV (4.47-13.85 meqO2/kg), and IV (111.51-51.39 I2/100 g) which demonstrated high coefficient of determination (R2) 0.84, 0.83, and 0.81, respectively. It can be summarized that FT-IR can be used as a novel tool for fast and noninvasive quality determination of frying oils.

Diagnosis of Lung Cancer by ATR-FTIR Spectroscopy and Chemometrics

Lung cancer is the leading cause of cancer-related death in the world. Early diagnosis has great significance for the survival of patients with lung cancer. In this paper, attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy combined with chemometrics was used to study the serum samples from patients with lung cancer and healthy people. The results of spectral band area comparison showed that the concentrations of protein, lipid and nucleic acids molecules in serum of patients with lung cancer were increased compared with those in healthy people. The original spectra were preprocessed to improve the accuracy of principal component regression (PCR) and partial least squares-discriminant analysis (PLS-DA) models. PLS-DA results for first derivative spectral data in nucleic acids (1250-1000cm-1) band showed 80% sensitivity, 91.89% specificity and 87.10% accuracy with highR c 2 of 0.8949 andR v 2 of 0.8153, low RMSEC of 0.3136 and RMSEV of 0.4180. It is shown that ATR-FTIR spectroscopy combined with chemometrics might be developed as a simple method for clinical screening and diagnosis of lung cancer.

An overview of recent advances and applications of FT-IR spectroscopy for quality, authenticity, and adulteration detection in edible oils

Authenticity and adulteration detection are primary concerns of various stakeholders, such as researchers, consumers, manufacturers, traders, and regulatory agencies. Traditional approaches for authenticity and adulteration detection in edible oils are time-consuming, complicated, laborious, and expensive; they require technical skills when interpreting the data. Over the last several years, much effort has been spent in academia and industry on developing vibrational spectroscopic techniques for quality, authenticity, and adulteration detection in edible oils. Among them, Fourier transforms infrared (FT-IR) spectroscopy has gained enormous attention as a green analytical technique for the rapid monitoring quality of edible oils at all stages of production and for detecting and quantifying adulteration and authenticity in edible oils. The technique has several benefits such as rapid, precise, inexpensive, and multi-analytical; hence, several parameters can be predicted simultaneously from the same spectrum. Associated with chemometrics, the technique has been successfully implemented for the rapid detection of adulteration and authenticity in edible oils. After presenting the fundamentals, the latest research outcomes in the last 10 years on quality, authenticity, and adulteration detection in edible oils using FT-IR spectroscopy will be highlighted and described in this review. Additionally, opportunities, challenges, and future trends of FT-IR spectroscopy will also be discussed.

Rapid qualitative identification and quantitative analysis of Flos Mume based on Fourier transform near infrared spectroscopy

Flos Mume, an ancient Chinese plant, is widely used for food and medicine. There are numerous varieties of Flos Mume, whose main active components are chlorogenic acid, hyperoside and isoquercitrin. Currently, Flos Mume varieties are mainly distinguished by physical appearance and they have not been scientifically indexed for identification. Fourier transform near infrared spectroscopy (FT-NIR) is a technique that when combined with chemometrics, determines internal components of samples and classifies them. Here, to distinguish between different Flos Mume varieties, we used a qualitative identification model based on FT-NIR. Various model parameters indicated its stability and high predictive performance. We developed a rapid, non-destructive method of simultaneously analyzing 8 components but found that only neochlorogenic acid, chlorogenic acid, rutin, hyperoside, and isoquercitrin have application value. Other components were excluded due to low concentration and poor prediction. Chemometric analysis found that chlorogenic acid become an ingredient which is quite different in the different categories. The content of chlorogenic acid were the highest among these components. Different varieties of Flos Mume were distinguished based on chlorogenic acid content, indicating that chlorogenic acid has potential to become a key indicator for application in quality evaluation. The established FT-NIR model for chlorogenic acid detection had excellent predictive capacity. FT-NIR was the first time applied to Flos Mume and our findings offer theoretical reference for the rapid identification and quantitative analysis of Flos Mume based on FT-NIR. Flos Mume could be evaluated for quality quickly and easily by means of FT-NIR spectroscopy.

Application of vibrational spectroscopic techniques for determination of thermal degradation of frying oils and fats: a review

Deep fried foods are popular among consumers due to their unique taste and texture. During the process of deep-frying, oil is subjected to a high temperature that results into the generation of harmful compounds. The repeated usage of frying oil is a common exercise and associated with various health hazards. Thus, determination of frying oil quality is a critical practice to follow. The chemical methods employed to determine the quality of frying oil are destructive and require large amount of harmful chemical, thus researchers are exploring the application of various vibrational spectroscopic techniques for this purpose. The first part of this review provides a detailed insight into fundamental theoretical aspects of two main vibrational spectroscopic techniques (infrared and Raman spectroscopy) and chemical alteration in frying oils under thermal stress. While in the following parts, the application of near-infrared (NIR) and Fourier transform infrared (FTIR) and Raman spectroscopy for evaluating the quality of various frying oils and fats under thermal stress has been discussed. It is anticipated that this review paper can serve as a reference source for impending research in this field.

Rapid Screening of Mentha spicata Essential Oil and L-Menthol in Mentha piperita Essential Oil by ATR-FTIR Spectroscopy Coupled with Multivariate Analyses

Mentha piperita essential oil (EO) has high economic importance because of its wide usage area and health-beneficial properties. Besides health-beneficial properties, Mentha piperita EO has great importance in the flavor and food industries because of its unique sensory and quality properties. High-valued essential oils are prone to being adulterated with economic motivations. This kind of adulteration deteriorates the quality of authentic essential oil, injures the consumers, and causes negative effects on the whole supply chain from producer to the consumer. The current research used fast, economic, robust, reliable, and effective ATR-FTIR spectroscopy coupled chemometrics of hierarchical cluster analysis(HCA), principal component analysis (PCA), partial least squares regression (PLSR) and principal component regression (PCR) for monitoring of Mentha spicata EO and L-menthol adulteration in Mentha piperita EOs. Adulterant contents (Mentha spicata and L-menthol) were successfully calculated using PLSR and PCR models. Standard error of the cross-validation SECV values changed between 0.06 and 2.14. Additionally, bias and press values showed alteration between 0.06 and1.43 and 0.03 and 41.15, respectively. Authentic Mentha piperita was successfully distinguished from adulterated samples, Mentha spicata and L-menthol, by HCA and PCA analysis. The results showed that attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy, coupled with chemometrics could be effectively used for monitoring various adulterants in essential oils.

Quantification of schizophyllan directly from the fermented broth by ATR-FTIR and PLS regression

Non-destructive methods that allow the quantification of bioproducts in a simple and quick manner during fermentation are extremely desirable from a practical point of view. Therefore, a 9 day fermentation experiment with Schizophyllum commune was carried out to investigate the possibility of using ATR-FTIR to quantify the schizophyllan biopolymer (SPG) directly from the culture medium. On each day, aliquots of the fermentation were taken, and the cell-free supernatant was analyzed by ATR-FTIR. The main objective of this step was to evaluate whether FTIR would be able to detect the appearance of specific peaks related to the production of SPG. The results of the PCA analysis showed that there was a reasonable separation of the days through the FTIR spectra. Then PCA-LDA was applied to the same dataset, which confirmed the formation of groups for each day of fermentation, after which, a calibration and test set was developed. Through a matrix generated by an experimental design with 2 factors and 5 levels, 25 samples were created with variations in the concentration of the culture medium and SPG. The ATR-FTIR spectra of this data set were modeled using PLS regression with backward selection of predictors. The results revealed that the amount of SPG produced can be quantified directly in the culture medium with excellent precision with R²CV = 0.951, R²P = 0.970, RMECV = 0.205 g, RMSEP = 0.170 g, RPDcv = 4.53 and RPDp = 5.88. The traditional method to quantify SPG is time consuming, requires several steps and uses solvents. In contrast, the method proposed in this work is a viable, faster, and a simpler alternative, which does not use reagents and does not require extensive pre-treatment of the samples.