Identification and quantification of goat milk adulteration using mid-infrared spectroscopy and chemometrics.

The fraudulent adulteration of goat milk with cheaper and more available milk of other species such as cow milk is occurrence. The aims of the present study were to investigate the effect of goat milk adulteration with cow milk on the mid-infrared (MIR) spectrum and further evaluate the potential of MIR spectroscopy to identify and quantify the goat milk adulterated. Goat milk was adulterated with cow milk at 5 different levels including 10%, 20%, 30%, 40%, and 50%. Statistical analysis showed that the adulteration had significant effect on the majority of the spectral wavenumbers. Then, the spectrum was preprocessed with standard normal variate (SNV), multiplicative scattering correction (MSC), Savitzky-Golay smoothing (SG), SG plus SNV, and SG plus MSC, and partial least squares discriminant analysis (PLS-DA) and partial least squares regression (PLSR) were used to establish classification and regression models, respectively. PLS-DA models obtained good results with all the sensitivity and specificity over 0.96 in the cross-validation set. Regression models using raw spectrum obtained the best result, with coefficient of determination (R2), root mean square error (RMSE), and the ratio of performance to deviation (RPD) of cross-validation set were 0.98, 2.01, and 8.49, respectively. The results preliminarily indicate that the MIR spectroscopy is an effective technique to detect the goat milk adulteration with cow milk. In future, milk samples from different origins and different breeds of goats and cows should be collected, and more sophisticated adulteration at low levels should be further studied to explore the potential and effectiveness of milk mid-infrared spectroscopy and chemometrics.

Quantitative predictions of protein and total flavonoids content in Tartary and common buckwheat using near-infrared spectroscopy and chemometrics.

A rapid method was developed for determining the total flavonoid and protein content in Tartary buckwheat by employing near-infrared spectroscopy (NIRS) and various machine learning algorithms, including partial least squares regression (PLSR), support vector regression (SVR), and backpropagation neural network (BPNN). The RAW-SPA-CV-SVR model exhibited superior predictive accuracy for both Tartary and common buckwheat, with a high coefficient of determination (R2p = 0.9811) and a root mean squared error of prediction (RMSEP = 0.1071) for flavonoids, outperforming both PLSR and BPNN models. Additionally, the MMN-SPA-PSO-SVR model demonstrated exceptional performance in predicting protein content (R2p = 0.9247, RMSEP = 0.3906), enhancing the effectiveness of the MMN preprocessing technique for preserving the original data distribution. These findings indicate that the proposed methodology could efficiently assess buckwheat adulteration analysis. It can also provide new insights for the development of a promising method for quantifying food adulteration and controlling food quality.

Quality Assessment of Reconstructed Cow, Camel and Mare Milk Powders by Near-Infrared Spectroscopy and Chemometrics.

Milk powders are becoming a major attraction for many industrial applications due to their nutritional and functional properties. Different types of powdered milk, each with their own distinct chemical compositions, can have different functionalities. Consequently, the development of rapid monitoring methods is becoming an urgent task to explore and expand their applicability. Lately, there is growing emphasis on the potential of near-infrared spectroscopy (NIRS) as a rapid technique for the quality assessment of dairy products. In the present work, we explored the potential of NIRS coupled with chemometrics for the prediction of the main functional and chemical properties of three types of milk powders, as well as their important processing parameters. Mare, camel and cow milk powders were prepared at different concentrations (5%, 10% and 12%) and temperatures (25 °C, 40 °C and 65 °C), and then their main physicochemical attributes and NIRS spectra were analyzed. Overall, high accuracy in both recognition and prediction based on type, concentration and temperature was achieved by NIRS-based models, and the quantification of quality attributes (pH, viscosity, dry matter content, fat content, conductivity and individual amino acid content) also resulted in high accuracy in the models. R2CV and R2pr values ranging from 0.8 to 0.99 and 0.7 to 0.98, respectively, were obtained by using PLSR models. However, SVR models achieved higher R2CV and R2pr values, ranging from 0.91 to 0.99 and 0.80 to 0.99, respectively.

Identification and quantitative detection of illegal additives in wheat flour based on near-infrared spectroscopy combined with chemometrics.

As a common food raw material in daily life, the quality and safety of wheat flour are directly related to people's health. In this study, a model was developed for the rapid identification and detection of three illegal additives in flour, namely azodicarbonamide (ADA), talcum powder, and gypsum powder. This model utilized a combination of near-infrared spectroscopy with chemometric methods. A one-dimensional convolutional neural network was used to reduce data dimensionality, while a support vector machine was applied for non-linear classification to identify illegal additives in flour. The model achieved a calibration set F1 score of 99.38% and accuracy of 99.63%, with a validation set F1 score of 98.81% and accuracy of 98.89%. Two cascaded wavelength selection methods were introduced: The first method involved backward interval partial least squares (BiPLS) combined with an improved binary particle swarm optimization algorithm (IBPSO). The second method utilized the CARS-IBPSO algorithm, which integrated competitive adaptive reweighted sampling (CARS) with IBPSO. The two cascade wavelength selection methods were used to select feature wavelengths associated with additives and construct partial least squares quantitative detection models. The models constructed using CARS-IBPSO selected feature wavelengths for detecting ADA, talcum powder, and gypsum powder exhibited the highest overall performance. The model achieved validation set determination coefficients of 0.9786, 0.9102, and 0.9226, with corresponding to root mean square errors of 0.0024%, 1.3693%, and 1.6506% and residual predictive deviations of 6.8368, 3.5852, and 3.9253, respectively. Near-infrared spectroscopy in combination with convolutional neural network dimensionality reduction and support vector machine classification enabled rapid identification of various illegal additives. The combination of CARS-IBPSO feature wavelength selection and partial least squares regression models facilitated rapid quantitative detection of these additives. This study introduces a new approach for rapidly and accurately identifying and detecting illegal additives in flour.

Structure-based chemical ontology improves chemometric prediction of antibacterial essential oils.

Plants are valuable resources for drug discovery as they produce diverse bioactive compounds. However, the chemical diversity makes it difficult to predict the biological activity of plant extracts via conventional chemometric methods. In this research, we propose a new computational model that integrates chemical composition data with structure-based chemical ontology. For a model validation, two training datasets were prepared from literature on antibacterial essential oils to classify active/inactive oils. Random forest classifiers constructed from the data showed improved prediction performance in both test datasets. Prior feature selection using hierarchical information criterion further improved the performance. Furthermore, an antibacterial assay using a standard strain of Staphylococcus aureus revealed that the classifier correctly predicted the activity of commercially available oils with an accuracy of 83% (= 10/12). The results of this study indicate that machine learning of chemical composition data integrated with chemical ontology can be a highly efficient approach for exploring bioactive plant extracts.

Quantitative determination of zearalenone in wheat by the CSA-NIR technique combined with chemometrics algorithms.

In the current study, a colorimetric sensor array combined with near-infrared (NIR) spectroscopy was used to quantitatively analyze zearalenone in wheat. The portable NIR spectrometer was used to scan the porphyrin reaction points of the wheat colorimetric sensor and collect spectral data. Subsequently, based on all the NIR spectral data, the two models and three feature selection algorithms are compared, and the best performance model and the best feature variable input are selected. Concurrently, the Kernel-based Extreme Learning Machine (KELM) model optimized by the two parameter optimization algorithms was compared, and the best parameter optimization algorithm was selected. Among all evaluation models, the KELM model optimized by the Competitive Adaptive Reweighted Sampling algorithm combined with the rime optimization algorithm has the best prediction effect. The predicted RP2 is 0.9900, and the root mean square error of prediction (RMSEP) is 18.4610 µg∙kg-1.

Chemometrics-based analysis of the phytochemical profile and antioxidant activity of Salvia species from Iran.

In recent years, the exploration of the therapeutic potential of Salvia has gained considerable attention, leading to a growing number of scientific studies emphasizing its pharmacological properties. Despite this, therapeutic applications of Salvia remain underexploited, requiring further investigation. Iran is a major center for sage diversity in Asia, boasting 60 Salvia species, 17 of which are unique to the area. This study aimed to comprehensively explore and compare the extracts of 102 Salvia samples belonging to 20 distinct Salvia species from Iran, providing a deeper understanding of their specific polyphenol content and, consequently, their antioxidant capabilities and potential therapeutic uses. All samples were analyzed to determine the contents of total phenolics, total flavonoids, total tannin, photosynthetic pigments, and ascorbic acid, along with their antioxidant activity. These data were then combined with the forty distinct chemical fingerprints identified by ultrafast high-pressure liquid chromatography coupled with high-resolution mass spectrometry. Multivariate data analysis was employed to find correlations and differences among the huge number of data obtained and to identify Salvia species with similar phytochemical and/or antioxidant properties. The results show that each Salvia species is characterized by a distinct class of polyphenols recognized for their antidiabetic, anti-inflammatory, cardioprotective and neuroprotective properties. Overall, our findings reveal the potential of some Salvia species for targeted therapeutic applications and provide a rational basis for the development of Salvia-derived nutraceuticals, ultimately improving the prospects for the use of Salvia in medicine.

Geographical origin determination of the PDO hazelnut (cv. Giresun Tombul) by chemometric analysis of FT-NIR and Raman spectra acquired from shell and kernel.

Turkey is the leading producer of hazelnuts, contributing to 62% of the total global production. Among 18 distinct local hazelnut cultivars, Giresun Tombul is the only cultivar that has received Protected Designation of Origin denomination from the European Comission (EC). However, there is currently no practical objective method to ensure its geographic origin. Therefore, in this study NIR and Raman spectroscopy, along with chemometric methods, such as principal component analysis, PLS-DA (partial least squares-discriminant analysis), and SVM-C (support vector machine-classification), were used to determine the geographical origin of the Giresun Tombul hazelnut cultivar. For this purpose, samples from unique 118 orchards were collected from eight different regions in Turkey during the 2021 and 2022 growing seasons. NIR and Raman spectra were obtained from both the shell and kernel of each sample. The results indicated that hazelnut samples exhibited distinct grouping tendencies based on growing season regardless of the spectroscopic technique and sample type (shell or kernel). Spectral information obtained from hazelnut shells demonstrated higher discriminative power concerning geographical origin compared to that obtained from hazelnut kernels. The PLS-DA models utilizing FT-NIR (Fourier transform near-infrared) and Raman spectra for hazelnut shells achieved validation accuracies of 81.7% and 88.3%, respectively, while SVM-C models yielded accuracies of 90.9% and 86.3%. It was concluded that the lignocellulosic composition of hazelnut shells, indicative of their geographic origin, can be accurately assessed using FT-NIR and Raman spectroscopy, providing a nondestructive, rapid, and user-friendly method for identifying the geographical origin of Giresun Tombul hazelnuts. PRACTICAL APPLICATION: The proposed spectroscopic methods offer a rapid and nondestructive means for hazelnut value chain actors to verify the geographic origin of Giresun Tombul hazelnuts. This could definitely enhance consumer trust by ensuring product authenticity and potentially help in preventing fraud within the hazelnut market. In addition, these methods can also be used as a reference for future studies targeting the authentication of other shelled nuts.

Exploring accurate mass measurements in pixel-based chemometrics: Advancing coffee classification with GC-HRMS-A proof of concept study.

This paper presents a study that assesses the application of chemometrics for classifying coffee samples in a quality control context. High-resolution and accurate mass measurements were utilized as input for pixel-based orthogonal partial least squares discriminant analysis (OPLS-DA) models. The compositional data were acquired through a fully automated workflow combining headspace solid-phase microextraction and gas chromatography-high-resolution mass spectrometry (GC-HRMS) using an FT-Orbitrap® mass analyzer. A workflow centered on accurate mass measurements was successfully utilized for group-type analysis, offering an alternative to methods relying solely on MS similarity searches. The predictive models underwent thorough evaluation, demonstrating robust multivariate classification performance. Five key coffee attributes, bitterness, acidity, body, intensity, and roasting level were successfully predicted using GC-HRMS data. The results revealed strong predictive accuracy across all models, ranging from 88.9 % (bitterness) to 94.4 % (roasting level). This study represents a significant advancement in automating methods for coffee quality control, notably increasing the predictive ability of the models compared to existing literature.

Smart Chemometric-Assisted Spectrophotometric Approaches for Simultaneous Quantification of Tertiary Combination Recommended for COVID-19 Supportive Treatments With Their Greenness Assessment.

BACKGROUND: There is an increasing interest of the scientific community in developing innovative methodologies for their analysis needs within a green analytical chemistry framework. UV spectrophotometry is one of the most promising eco-friendly methods, which is integrated with advanced chemometric tools to enhance the selectivity of the analysis of complex mixtures with severe overlapped signals. OBJECTIVE: Simultaneous determination of a triple-combination of pseudoephedrine hydrochloride (PSE), carbinoxamine maleate (CRX), and paracetamol (PAR) using an artificial intelligence system and multivariate calibration methods. This combination has been recently recommended for COVID-19 home-treated patients as part of a symptomatic treatment. METHODS: Namely, the suggested models are artificial neural networks, partial least-squares, and principal component regression. The proposed algorithms were optimized and developed with the aid of a five-level, three-factor experimental design. RESULTS: The investigated methods were applied over the concentration range of 100-180 µg/mL, 18-16 µg/mL, and 4-12 µg/mL for PSE, CRX, and PAR, respectively. The models' validation results demonstrated excellent recoveries (around 98 to 102%), signaling the approaches' outstanding resolution capacity for the cited compounds in the presence of common excipients. The outcomes of the studied methods were statistically compared to the official approaches, and no significant difference was found. CONCLUSIONS: The suggested models were efficiently employed to determine the selected drugs in their combined tablets without any initial separation steps. The impact of these methods on the environment was evaluated via greenness tools: namely, the National Environmental Method Index, Raynie and Driver's green assessment method, Analytical Eco-Scale, Green Analytical Procedure Index, and Analytical Greenness Metric. HIGHLIGHTS: Green chemometric quality assessment of PSE, CRX, and PAR in their pure and pharmaceutical dosage forms. The established approaches are innovative, sustainable, smart, fast, selective, and cost-effective. These models are potential green nominees for routine analysis of the investigated mixture in quality control laboratories.

A chemometric approach to assess the oil composition and content of microwave-treated mustard (Brassica juncea) seeds using Vis-NIR-SWIR hyperspectral imaging.

The wide gap between the demand and supply of edible mustard oil can be overcome to a certain extent by enhancing the oil-recovery during mechanical oil expression. It has been reported that microwave (MW) pre-treatment of mustard seeds can have a positive effect on the availability of mechanically expressible oil. Hyperspectral imaging (HSI) was used to understand the change in spatial spread of oil in the microwave (MW) treated seeds with bed thickness and time of exposure as variables, using visible near-infrared (Vis-NIR, 400-1000 nm) and short-wave infrared (SWIR, 1000-1700 nm) systems. The spectral data was analysed using chemometric techniques such as partial least square discriminant analysis (PLS-DA) and regression (PLSR) to develop prediction models. The PLS-DA model demonstrated a strong capability to classify the mustard seeds subjected to different MW pre-treatments from control samples with a high accuracy level of 96.6 and 99.5% for Vis-NIR and SWIR-HSI, respectively. PLSR model developed with SWIR-HSI spectral data predicted (R2 > 0.90) the oil content and fatty acid components such as oleic acid, erucic acid, saturated fatty acids, and PUFAs closest to the results obtained by analytical techniques. However, these predictions (R2 > 0.70) were less accurate while using the Vis-NIR spectral data.

A Comprehensive Analysis of Fel Ursi and Its Common Adulterants Based on UHPLC-QTOF-MSE and Chemometrics.

BACKGROUND: As one of the four most valuable animal medicines, Fel Ursi, named Xiong Dan (XD) in China, has the effect of clearing heat, calming the liver, and brightening the eyes. However, due to the special source of XD and its high price, other animals' bile is often sold as XD or mixed with XD on the market, seriously affecting its clinical efficacy and consumers' rights and interests. In order to realize identification and adulteration analysis of XD, UHPLC-QTOF-MSE and multivariate statistical analysis were used to explore the differences in XD and six other animals' bile. METHODS: XD, pig gall (Zhu Dan, ZD), cow gall (Niu Dan, ND), rabbit gallbladder (Tu Dan, TD), duck gall (Yan Dan, YD), sheep gall (Yang Dan, YND), and chicken gall (Ji Dan, JD) were analyzed by UHPLC-QTOF-MSE, and the MS data, combined with multivariate analysis methods, were used to distinguish between them. Meanwhile, the potential chemical composition markers that contribute to their differences were further explored. RESULTS: The results showed that XD and six other animals' bile can be distinguished from each other obviously, with 27 ions with VIP > 1.0. We preliminarily identified 10 different bile acid-like components in XD and the other animals' bile with significant differences (p < 0.01) and VIP > 1.0, such as tauroursodeoxycholic acid, Glycohyodeoxycholic acid, and Glycodeoxycholic acid. CONCLUSIONS: The developed method was efficient and rapid in accurately distinguishing between XD and six other animals' bile. Based on the obtained chemical composition markers, it is beneficial to strengthen quality control for bile medicines.

Rapid identification of peanut oil adulteration by near infrared spectroscopy and chemometrics.

Peanut oil, prized for its unique taste and nutritional value, grapples with the pressing issue of adulteration by cost-cutting vendors seeking higher profits. In response, we introduce a novel approach using near-infrared spectroscopy to non-invasively and cost-effectively identify adulteration in peanut oil. Our study, analyzing spectral data of both authentic and intentionally adulterated peanut oil, successfully distinguished high-quality pure peanut oil (PPEO) from adulterated oil (AO) through rigorous analysis. By combining near-infrared spectroscopy with factor analysis (FA) and partial least squares regression (PLS), we achieved discriminant accuracies exceeding 92 % (S > 2) and 89 % (S > 1) for FA models 1 and 2, respectively. The PLS model demonstrated strong predictive capabilities, with a prediction coefficient (R2) surpassing 93.11 and a root mean square error (RMSECV) below 4.43. These results highlight the effectiveness of NIR spectroscopy in confirming the authenticity of peanut oil and detecting adulteration in its composition.

Online monitoring for extraction of Tibetan medicine Meconopsis quintuplinervia regel. Based on near infrared spectroscopy coupled with chemometrics.

The extraction process plays a crucial role in the production of Tibetan medicines. This study focused on assembling a set of online near-infrared (NIR) spectroscopy detection devices for the extraction of medicinal herbs. The original infrared device was transformed into an online detection system. After evaluating the stability of the system, we applied online NIR spectroscopy monitoring to the flavonoid contents (total flavonoids, quercetin-3-O-sophoroside, and luteolin) of Meconopsis quintuplinervia Regel. during the ultrasonic extraction process and determined the extraction endpoint. Nine batches of samples were employed to construct quantitative and discriminant models, half of the remaining two batches of samples are used for external verification. Our research shows that the residual predictive deviation (RPD) values of total flavonoids, quercetin-3-O-sophoroside and luteolin models exceeded 2.5. The R values for external verification of the three ingredients were above 0.9, with RPD values generally exceeding 2 and RSEP values within 10 %, demonstrating the model's strong predictive performance. Most of the extraction endpoints of the flavonoid components in M. quintuplinervia ranged from 18 to 58 min, with high consistency between the predicted extraction endpoints of the external validation, suggesting accurate determination of extraction endpoints based on predicted values. This study can provide a reference for the online NIR spectroscopy quality monitoring of the extraction process of Chinese and Tibetan herbs.

Contamination of trace, non-essential/heavy metals in nutraceuticals/dietary supplements: A chemometric modelling approach and evaluation of human health risk upon dietary exposure.

Across the world, nutraceuticals/dietary supplements are commonly consumed without medical supervision, and believing these products are harmless to health. However, these products may contain trace (TMs) and non-essential/heavy metals (nHMs) as contaminants at levels higher than the recommended daily allowance (RDA), which can be hazardous to human health. Consequently, it is crucial to assess the levels of these metals to ensure the safety of these products. This study aimed to analyze the concentration of TMs (Mn, Cu and Zn) and nHMs (Al, Cr, Ni, Cd and Pb) in nutraceuticals/dietary supplements. Metal analysis was conducted using inductively coupled plasma-optical emission spectrometry (ICP-OES). Multivariate and bivariate analysis including principle component analysis (PCA), hierarchical cluster analysis (HCA) and Pearson correlation coefficient (PCC) were applied to understand inter-metal association and sources of these metals. Concentration ranges for TMs were found as, Mn (0.2-4.3 mg/kg), Cu (0.11-2.54 mg/kg), and Zn (0.1-22.66 mg/kg) while the nHMs concentration ranges were: Al (0.046-3.336 mg/kg), Cr (0.11-1.63 mg/kg), Ni (0.18-0.72 mg/kg), Cd (0.04-0.92 mg/kg), and Pb (0.18-1.08 mg/kg). The levels of tolerable dietary intake (TDI) for Cr and Ni, and the provisional tolerable monthly intake (PTMI) limit for Cd, exceeded the values set by the World Health Organization (WHO) and the European Food Safety Authority (EFSA). The estimation of the target hazard quotient (THQ <1), hazard index (HI < 1) and cumulative cancer risk (CCR <1 ✕ 10-3) indicated no significant non-carcinogenic and carcinogenic health risks associated with consuming these products. Therefore, the primary recommendation from this study is to use the nutraceuticals/dietary supplements should be under the supervision of dietitian.

A digital image smartphone-based approach to Slovak Tokaj wine authentication chemometric assisted.

The authentication of Slovak wines in comparison to other similar wines from various geographical regions, namely Hungary, France, Austria, and Ukraine, was conducted using the OC-PLS, DD-SIMCA, and PLS-DM models, all of them operating in rigorous way. The study involved 63 samples, of which 41 originated from Slovakia, covering diverse wine types such as varietal wines, cuvée selections (different "putnový"), and essence. To capture digital images under controlled conditions, a custom-made cardboard box with white inner surfaces was devised and equipped with a smartphone. During the training phase, sensitivities of 96%, 100%, and 96% were attained for OC-PLS, DD-SIMCA, and PLS-DM, respectively. In the subsequent stages of validation and testing for DD-SIMCA and PLS-DM, the proposed methods displayed optimal efficiency, achieving both sensitivity and specificity rates of 100%. However, such results were not achieved in the case of OC-PLS, which exhibited efficiency levels of 90% in validation and 80% in testing.

Rapid analysis of bioactive compounds from citrus samples by direct analysis in real-time mass spectrometry combined with chemometrics.

Renowned for their nutritional benefits, citrus fruits are harvested at various stages in China for functional food production. This study introduces an innovative analytical method, DART-MS, enabling direct qualitative analysis of citrus samples without the need for preprocessing. Simultaneously, the combination of chemometrics can be applied to distinguish between three different citrus samples: Citri Reticulatae Pericarpium, Citri Reticulatae Pericarpium Viride, and Citri Reticulatae "Chachi". Notably, given the international regulatory concerns surrounding synephrine, a precise quantitative analysis method for synephrine was developed. The limit of detection (LOD) and the limit of quantification (LOQ) were 39 ng mL-1 and 156 ng mL-1, respectively. The recovery rates obtained varied from 98.46% to 100.71%. Furthermore, the intra-day and inter-day precision demonstrated robust consistency, with values spanning 5.0-6.1% and 5.03-6.08%, respectively, offering quicker results compared to those from HPLC-MS, promising a safer assessment of herbal and food products.

Rapid non-destructive monitoring and quality assessment of the fumigation process of Shanxi aged vinegar based on Vis-NIR hyperspectral imaging combined with multiple chemometric algorithms.

The quality of the grains during the fumigation process can significantly affect the flavour and nutritional value of Shanxi aged vinegar (SAV). Hyperspectral imaging (HSI) was used to monitor the extent of fumigated grains, and it was combined with chemometrics to quantitatively predict three key physicochemical constituents: moisture content (MC), total acid (TA) and amino acid nitrogen (AAN). The noise reduction effects of five spectral preprocessing methods were compared, followed by the screening of optimal wavelengths using competitive adaptive reweighted sampling. Support vector machine classification was employed to establish a model for discriminating fumigated grains, and the best recognition accuracy reached 100%. Furthermore, the results of partial least squares regression slightly outperformed support vector machine regression, with correlation coefficient for prediction (Rp) of 0.9697, 0.9716, and 0.9098 for MC, TA, and AAN, respectively. The study demonstrates that HSI can be employed for rapid non-destructive monitoring and quality assessment of the fumigation process in SAV.

Spectroscopy-based chemometrics combined machine learning modeling predicts cashew foliar macro- and micronutrients.

Precision nutrient management in orchard crops needs precise, accurate, and real-time information on the plant's nutritional status. This is limited by the fact that it requires extensive leaf sampling and chemical analysis when it is to be done over more extensive areas like field- or landscape scale. Thus, rapid, reliable, and repeatable means of nutrient estimations are needed. In this context, lab-based remote sensing or spectroscopy has been explored in the current study to predict the foliar nutritional status of the cashew crop. Novel spectral indices (normalized difference and simple ratio), chemometric modeling, and partial least square regression (PLSR) combined machine learning modeling of the visible near-infrared hyperspectral data were employed to predict macro- and micronutrients content of the cashew leaves. The full dataset was divided into calibration (70 % of the full dataset) and validation (30 % of the full dataset) datasets. An independent validation dataset was used for the validation of the algorithms tested. The approach of spectral indices yielded very poor and unreliable predictions for all eleven nutrients. Among the chemometric models tested, the performance of the PLSR was the best, but still, the predictions were not acceptable. The PLSR combined machine learning modeling approach yielded acceptable to excellent predictions for all the nutrients except sulphur and copper. The best predictions were observed when PLSR was combined with Cubist for nitrogen, phosphorus, potassium, manganese, and zinc; support vector machine regression for calcium, magnesium, iron, copper, and boron; elastic net for sulphur. The current study showed hyperspectral remote sensing-based models could be employed for non-destructive and rapid estimation of cashew leaf macro- and micro-nutrients. The developed approach is suggested to employ within the operational workflows for site-specific and precision nutrient management of the cashew orchards.

Analysis and authentication of avocado oil by low-field benchtop NMR spectroscopy and chemometrics.

Avocado oil is a nutritious, edible oil produced from avocado fruit. It has high commercial value and is increasing in popularity, thus powerful analytical methods are needed to ensure its quality and authenticity. Recent advancements in low-field (LF) NMR spectroscopy allow for collection of high-quality data despite the use of low magnetic fields produced by non-superconductive magnets. Combined with chemometrics, LF NMR opens new opportunities in food analysis using targeted and untargeted approaches. Here, it was used to determine poly-, mono-, and saturated fatty acids in avocado oil. Although direct signal integration of LF NMR spectra was able to determine certain classes of fatty acids, it had several challenges arising from signal overlapping. Thus, we used partial least square regression and developed models with good prediction performance for fatty acid composition, with residual prediction deviation ranging 3.46-5.53 and root mean squared error of prediction CV ranging 0.46-2.48. In addition, LF NMR, combined with unsupervised and supervised methods, enabled the differentiation of avocado oil from other oils, namely, olive oil, soybean oil, canola oil, high oleic (OL) safflower oil, and high OL sunflower oil. This study showed that LF NMR can be used as an efficient alternative for the compositional analysis and authentication of avocado oil. PRACTICAL APPLICATION: Here, we describe the application of LF-NMR for fatty acid analysis and avocado oil authentication. LF-NMR can be an efficient tool for targeted and untargeted analysis, thus becoming an attractive option for companies, regulatory agencies, and quality control laboratories. This tool is especially important for organizations and entities seeking economic, user-friendly, and sustainable analysis solutions.