FT-NIR spectroscopy coupled with multivariate analysis for detection of starch adulteration in turmeric powder

Nondestructive identification and classification of starch types based on multispectral techniques coupled with chemometrics

Starch is the main source of energy and nutrition. Therefore, some merchants often illegally add cheaper starches to other types of starches or package cheaper starches as higher priced starches to raise the price. In this study, 159 samples of commercially available wheat starch, potato starch, corn starch and sweet potato starch were selected for the identification and classification based on multispectral techniques, including near-infrared (NIR), mid-infrared (MIR) and Raman spectroscopy combined with chemometrics, including pretreatment methods, characteristic wavelength selection methods and classification algorithms. The results indicate that all three spectral techniques can be used to discriminate starch types. The Raman spectroscopy demonstrated superior performance compared to that of NIR and MIR spectroscopy. The accuracy of the models after characteristic wavelength selection is generally superior to that of the full spectrum, and two-dimensional correlation spectroscopy (2D-COS) achieves better model performance than other wavelength selection methods. Among the four classification methods, convolutional neural network (CNN) exhibited the best prediction performance, achieving accuracies of 99.74 %, 97.57 % and 98.65 % in NIR, MIR and Raman spectra, respectively, without pretreatment or characteristic wavelength selection.

Development of non-destructive methods for the assessment of authenticity of sports whey protein supplements

In the category of sports supplements, whey protein powder is one of the popular supplements for muscle building applications. Therefore, verification of the sport supplements as authentic products has become a universal concern. This work aimed to propose vibrational spectroscopy including near infrared (NIR) and infrared (IR) as rapid and non-destructive testing tools for the detection and quantification of maltodextrin, milk powder and milk whey powder in whey protein supplements. Initially, principal component analysis was applied to data for pattern recognition and the results displayed a fine pattern of discrimination. Partial least square discrimination analysis (PLS-DA) and K-nearest neighbours (KNN) were exploited as supervised method modelling classification. This process was done in order to respond to two vital questions whether the sample is adulterated or not and what is the kind of adulteration. PLS-DA showed better classification results rather than KNN according to the figure of merits of the model. Partial least square regression (PLSR) was employed on pre-treated spectra to quantify the amount of adulteration in sport whey supplements. Eventually, it seems vibrational spectroscopy could be implemented as a simple, and low-cost analysis method for the detection and quantification of mentioned adulterants in whey protein supplements.

Machine Learning-Enabled NIR Spectroscopy. Part 3: Hyperparameter by Design (HyD) Based ANN-MLP Optimization, Model Generalizability, and Model Transferability

Data variations, library changes, and poorly tuned hyperparameters can cause failures in data-driven modelling. In such scenarios, model drift, a gradual shift in model performance, can lead to inaccurate predictions. Monitoring and mitigating drift are vital to maintain model effectiveness. USFDA and ICH regulate pharmaceutical variation with scientific risk-based approaches. In this study, the hyperparameter optimization for the Artificial Neural Network Multilayer Perceptron (ANN-MLP) was investigated using open-source data. The design of experiments (DoE) approach in combination with target drift prediction and statistical process control (SPC) was employed to achieve this objective. First, pre-screening and optimization DoEs were conducted on lab-scale data, serving as internal validation data, to identify the design space and control space. The regression performance metrics were carefully monitored to ensure the right set of hyperparameters was selected, optimizing the modelling time and storage requirements. Before extending the analysis to external validation data, a drift analysis on the target variable was performed. This aimed to determine if the external data fell within the studied range or required retraining of the model. Although a drift was observed, the external data remained well within the range of the internal validation data. Subsequently, trend analysis and process monitoring for the mean absolute error of the active content were conducted. The combined use of DoE, drift analysis, and SPC enabled trend analysis, ensuring that both current and external validation data met acceptance criteria. Out-of-specification and process control limits were determined, providing valuable insights into the model's performance and overall reliability. This comprehensive approach allowed for robust hyperparameter optimization and effective management of model lifecycle, crucial in achieving accurate and dependable predictions in various real-world applications.

Spice and Herb Frauds: Types, Incidence, and Detection: The State of the Art

There is a necessity to protect the quality and authenticity of herbs and spices because of the increase in the fraud and adulteration incidence during the last 30 years. There are several aspects that make herbs and spices quite vulnerable to fraud and adulteration, including their positive and desirable sensorial and health-related properties, the form in which they are sold, which is mostly powdered, and their economic relevance around the world, even in developing countries. For these reasons, sensitive, rapid, and reliable techniques are needed to verify the authenticity of these agri-food products and implement effective adulteration prevention measures. This review highlights why spices and herbs are highly valued ingredients, their economic importance, and the official quality schemes to protect their quality and authenticity. In addition to this, the type of frauds that can take place with spices and herbs have been disclosed, and the fraud incidence and an overview of scientific articles related to fraud and adulteration based on the Rapid Alert System Feed and Food (RASFF) and the Web of Science databases, respectively, during the last 30 years, is carried out here. Next, the methods used to detect adulterants in spices and herbs are reviewed, with DNA-based techniques and mainly spectroscopy and image analysis methods being the most recommended. Finally, the available adulteration prevention measurements for spices and herbs are presented, and future perspectives are also discussed.

Hybrid method for accurate starch estimation in adulterated turmeric using Vis-NIR spectroscopy

Turmeric is widely used as a health supplement and foodstuff in South East Asian countries because of its medicinal benefits. Like several other plants and peppers, turmeric is prone to exploitation because of its economic value, rising consumer need, and essential food element that adds colour and flavour. Due to this, quick and comprehensive testing processes are needed to detect adulterants in turmeric. In this study, pure turmeric powders were mixed with starch in proportions ranging from 0 to 50% with a 1% variation to obtain different combinations. Reflectance spectra of pure turmeric and starch mixed samples were recorded using a JASCO-V770 spectrometer from 400 to 2050 nm. The recorded spectra were pre-processed using a Multiplicative Scatter Correction (MSC) and Standard Normal Variate (SNV). The Savitzky-Golay (SG) filter was initially applied to these original (X), MSC, and SNV-corrected spectra. Secondly, the Extra Tree Regressor (ETR) feature selection method was employed to select the best features. Finally, principal component analysis (PCA) was used to reduce the dimension of the selected features. The stacked generalization method was applied to improve the performance of this work. Both regressors and classifier stacking techniques have been tested with different classification and regression methods. The K-Nearest Neighbours (KNN), Decision Tree (DT), and Random Forest (RF) models were used as base learners, and Logistic Regression (LRC) was used as a meta-model for classification and Linear Regression (LR) for regression analysis. The proposed method achieved the best regression performance with r2 of 0.999, Root Mean Square Error (RMSE) of 0.206, Ratio of Performance to Deviation (RPD) of 73.73, and Range Error Ratio (RER) of 480.58, whereas 100% F1 score and Matthew's Correlation Coefficient (MCC) classification performance.

Rapid Determination of Nutmeg Shell Content in Ground Nutmeg Using FT-NIR Spectroscopy and Machine Learning

Nutmeg is a popular spice often used in ground form, which makes it highly susceptible to food fraud. Therefore, the aim of the present study was to detect adulteration of ground nutmeg with nutmeg shell via Fourier transform near-infrared (FT-NIR) spectroscopy. For this purpose, 36 authentic nutmeg samples and 10 nutmeg shell samples were analyzed pure and in mixtures with up to 50% shell content. The spectra plot as well as a principal component analysis showed a clear separation trend as a function of shell content. A support vector machine regression used for shell content prediction achieved an R2 of 0.944 in the range of 0-10%. The limit of detection of the prediction model was estimated to be 1.5% nutmeg shell. Based on random sub-sampling, the likelihood was found to be 2% that a pure nutmeg sample is predicted with a nutmeg shell content of >1%. The results confirm the suitability of FT-NIR spectroscopy for rapid detection and quantitation of the shell content in ground nutmeg.

Non-destructive determination of grass pea and pea flour adulteration in chickpea flour using near-infrared reflectance spectroscopy and chemometrics

BACKGROUND

In order to obtain more economic gains, some food products are adulterated with low-cost substances, if they are toxic, they may pose public health risks. This has called forth the development of quick and non-destructive methods for detection of adulterants in food. Near-infrared reflectance spectroscopy (NIRS) has become a promising tool to detect adulteration in various commodities. We have developed rapid NIRS based analytical methods for quantification of two cheap adulterants (grass pea and pea flour) in a popular Indian food material, chickpea flour.

RESULTS

The NIRS spectra of pure chickpea, pure grass pea, pure pea flour and adulterated samples of chickpea flour with grass pea and pea flour (1-90%) (w/w) were acquired and preprocessed. Calibration models were built based on modified partial least squares regression (MPLSR), partial least squares (PLS), principal component regression (PCR) methods. Based on lowest values of standard error of calibration (SEC) and standard error of cross-validation (SECV), MPLSR-NIRS models were selected. These models exhibited coefficient of determination (R2 ) of 0.999, 0.999, SEC of 0.905, 0.827 and SECV of 1.473, 1.491 for grass pea and pea, respectively. External validation revealed R2 and standard error of prediction (SEP) of 0.999 and 1.184, 0.997 and 1.893 for grass pea and pea flour, respectively.

CONCLUSION

The statistics confirmed that our MPLSR-NIRS based methods are quite robust and applicable to detect grass pea and pea flour adulterants in chickpea flour samples and have potential for use in detecting food fraud. © 2022 Society of Chemical Industry.

Prediction of maize flour adulteration in chickpea flour (besan) using near infrared spectroscopy

The present study was performed to develop Near-infrared spectroscopy based prediction method for the quantification of the maize flour adulteration in chickpea flour. Adulterated samples of Chickpea flour (besan) were prepared by spiking different concentrations of maize flour with pure Chickpea flour in the range of 1–90% (w/w). The spectra of pure Chickpea flour, pure maize flour, and adulterated samples of Chickpea flour with maize flour were acquired as the logarithm of reciprocal of reflectance (log 1/R) in the entire Visible-NIR wavelength range of 400–2498 nm. The acquired spectra were pre-processed by Ist derivative, standard normal variate, and detrending. The calibration models were developed using modified partial least square regression (MPLSR), partial least square regression and principal component regression. The optimal model was selected on the basis of highest values of the coefficient of determination (RSQ), one minus variance ratio (1-VR) and lowest values of standard errors of calibration (SEC), and standard error of cross-validation (SECV). MPLSR model having RSQ and 1-VR value of 0.999 and 0.996 having SEC and SECV value of 1.092 and 2.042 was developed for quantification of maize flour adulteration in chickpea flour. Cross validation and external validation of the developed models resulted in RSQ of 0.999, 0.997 and standard error of prediction of 1.117, and 2.075, respectively.

Fluorescence quenching by competitive absorption between solid foods: Rapid and non-destructive determination of maize flour adulterated in turmeric powder

Fluorescence quenching induced by competitive absorption between different components of solid foods was observed for the first time. By using front-face synchronous fluorescence spectroscopy (FFSFS) and fluorescence titration, competitive absorption between maize flour and turmeric powder was proven to occur between phenolic acids in maize flour and curcumin in turmeric powder. FFSFS was applied for the rapid and non-destructive determination of maize flour adulterated in turmeric powder. Prediction models were constructed by partial least square (PLS) regression based on unfolded total synchronous fluorescence spectra, and were validated by five-fold cross-validation and external validation, with the determination coefficient of prediction (R_p²) greater than 0.95, root mean square error of prediction (RMSEP) < 6%, relative error of prediction (REP) < 15% and residual predictive deviation (RPD) greater than 5. The limit of detection (LOD) of maize flour was approximately 9%. In addition, most relative errors for test samples were from -20% to 20%.

An evaluation of IR spectroscopy for authentication of adulterated turmeric powder using pattern recognition

Turmeric powder is a widely consumed spice, making it an attractive target for adulteration, which is not easily detected. The study examined the simultaneous use of IR spectroscopy in combination with controlled (PCA) and uncontrolled (PLS-DA and CMCA) pattern recognition techniques to detect and classify Sudan Red, starch and metanil yellow fraud in turmeric powder nondestructively. The results showed that the two major peaks in turmeric powder at 1625 cm^-1 and 1600 cm^-1 are not present in Sudan Red, starch and metanil yellow because these materials lack this functional group. Data distribution at the two PC locations showed clearly scattered clusters according to the four mixing studied models (turmeric powder, turmeric powder-Sudan Red mixture, turmeric powder-starch mixture and turmeric powder-metanil yellow mixture), but there was a clear overlap between turmeric powder and turmeric powder - Sudan red mixture. Both PLS-DA and SIMCA supervised methods showed satisfactory discrimination. The results also showed that in all the sample groups, when the samples were classified by PLS-DA, the values were higher compared to the SIMCA model. The overall precision of the SIMCA and PLS-DA classifier were 82% and 92%, respectively. However, when considering only two main categories adulterated (the samples at the groups 2, 3 and 4) and pure (the samples at the group 1), an acceptable degree of separation between the resulting classes was obtained. Consequently, IR spectroscopy with pattern recognition methods was found to be a promising tool for nondestructive grouping of turmeric powder samples with different types of adulteration in turmeric powder.

Fraud Detection of Herbal Medicines Based on Modern Analytical Technologies Combine with Chemometrics Approach: A Review

Fraud in herbal medicines (HMs), commonplace throughout human history, is significantly related to medicinal effects with sometimes lethal consequences. Major HMs fraud events seem to occur with a certain regularity, such as substitution by counterfeits, adulteration by addition of inferior production-own materials, adulteration by chemical compounds, and adulteration by addition of foreign matter. The assessment of HMs fraud is in urgent demand to guarantee consumer protection against the four fraudulent activities. In this review, three analysis platforms (targeted, non-targeted, and the combination of non-targeted and targeted analysis) were introduced and summarized. Furthermore, the integration of analysis technology and chemometrics method (e.g., class-modeling, discrimination, and regression method) have also been discussed. Each integration shows different applicability depending on their advantages, drawbacks, and some factors, such as the explicit objective analysis or the nature of four types of HMs fraud. In an attempt to better solve four typical HMs fraud, appropriate analytical strategies are advised and illustrated with several typical studies. The article provides a general workflow of analysis methods that have been used for detection of HMs fraud. All analysis technologies and chemometrics methods applied can conduce to excellent reference value for further exploration of analysis methods in HMs fraud.

Rapid detection and quantification of adulteration in Chinese hawthorn fruits powder by near-infrared spectroscopy combined with chemometrics

The aim of this study is to explore the feasibility of detection and quantification of two cheap adulterants (maltodextrin and starch) in Chinese functional food, hawthorn fruits powder (HFP), by using near infrared (NIR) spectroscopy coupled with chemometrics methods. The partial least squares discriminant analysis (PLS-DA) models were developed to discriminate the adulterated HFP from the authentic HFP, while the partial least squares regression (PLSR) models were employed to determine the contents of adulterants. In order to yield the best results, various spectra pretreatment methods and wavelength selection methods were carefully investigated. The models' qualities were assessed by the self-consistency test, the independent test and the rigorous leave-one-out cross-validation test. The metrics for the PLS-DA discriminative model included error rate, true positive rate, true negative rate and F1 score, while the metrics for the PLSR quantitative model were determination coefficient, root mean square error and residual prediction deviation. Finally, very satisfying results were obtained, which indicate that our method is quite robust and applicable, and thus has great potential for rapid detection of adulteration in powder of many other herbal plants or functional foods.

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

Attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy integrated with chemometrics was effectively applied for the rapid detection and accurate quantification of fried mustard oil (FMO) adulteration in pure mustard oil (PMO). PMO was adulterated with FMO in the range of 0.5-50% v/v. Principal component analysis (PCA) elucidated the studied adulteration using two components with an explained variance of 97%. The linear discriminant analysis (LDA) was adopted to classify the adulterated PMO samples with FMO. LDA model showed 100% accuracy initially, as well as when cross-validated. To enhance the overall quality of models, characteristic spectral regions were optimized, and principal component regression (PCR) and partial least square regression (PLS-R) models were constructed with high accuracy and precision. PLS-R model for the 2nd derivative of the optimized spectral region 1260-1080 cm^-1 showed best results for prediction sample sets in terms of high R² and residual predictive deviation (RPD) value of 0.999 and 31.91 with low root mean square error (RMSE) and relative prediction error (RE %) of 0.53% v/v and 3.37% respectively. Thus, the suggested method can detect up to 0.5% v/v of adulterated FMO in PMO in a short time interval.

Identification and Quantification of Turmeric Adulteration in Egg-Pasta by Near Infrared Spectroscopy and Chemometrics

“Egg pasta” is a kind of pasta prepared by adding eggs in the dough; the color of this product is often associated to its quality, as it is proportional to the quantity of egg present in the dough. A possible adulteration on this product is represented by the addition of turmeric (not reported in the label) in the dough. The inclusion of this ingredient (which is minimal, given the strong coloring power of this spice) fraudulently accentuates the yellow color of the product, making it more attractive to the consumer. Given this scenario, the aim of the present work is to develop an analytical approach suitable at detecting the presence of turmeric as an adulterant in egg pasta. One hundred samples of traditional and adulterated egg pasta were analyzed by NIR spectroscopy and PLS-DA (Partial Least Squares Discriminant Analysis) in order to discriminate adulterated and compliant pasta. The classification model provided a total correct classification rate of 97.5% in external validation (40 samples). Eventually, the adulterant was quantified by PLS. This strategy provided satisfying results, achieving a RMSEP (Root Mean Square Error in Prediction) of 0.112 (%-w/w) in external validation.