Level Of Adulteration Research Articles

Discrimination of wild edible and poisonous fungi with similar appearance (Cantharellus albovenosus vs Omphalotus olearius) based on lipidomics using UHPLC-HR-AM/MS/MS

Mushrooms have long been appreciated as a delicacy as well as used as vital dietary supplements and food. However, poisoning from mushroom consumption remains a worldwide health problem, resulting in morbidity and mortality. In this paper, for the first time an untargeted lipidomics analysis on Omphalotus olearius (O. olearius) and Cantharellus albovenosus (C. albovenosus) was performed by ultra-high-performance liquid chromatography high-resolution accurate mass-mass spectrometry (UHPLC-HR-AM/MS/MS). A total of 350 lipid molecular species from 21 classes were detected in the two mushrooms. Based on multivariate and univariate analyses, 37 differential lipids were selected as metabolic markers for distinguishing the two mushrooms. And only 5 % (w/w) adulteration level was sufficient to identify C. albovenosus mixed with O. olearius. This study indicated that lipidomics approaches can provide high-resolution markers for food safety evaluation, which can be used as an accurate evaluation for distinguishing poisonous mushrooms from edible ones.

Rapid Authentication of Intact Stingless Bee Honey (SBH) by Portable LED-Based Fluorescence Spectroscopy and Chemometrics.

Indonesian stingless bee honey (SBH) of Geniotrigona thoracica is popular and traded at an expensive price. Brown rice syrup (RS) is frequently used as a cheap adulterant for an economically motivated adulteration (EMA) in SBH. In this study, authentic Indonesian Geniotrigona thoracica SBH of Acacia mangium (n = 100), adulterated SBH (n = 120), fake SBH (n = 100), and RS (n = 200) were prepared. In short, 2 mL of each sample was dropped directly into an innovative sample holder without any sample preparation including no dilution. Fluorescence intensity was acquired using a fluorescence spectrometer. This portable instrument is equipped with a 365 nm LED lamp as the fixed excitation source. Principal component analysis (PCA) was calculated for the smoothed spectral data. The results showed that the authentic SBH and non-SBH (adulterated SBH, fake SBH, and RS) samples could be well separated using the smoothed spectral data. The cumulative percentage variance of the first two PCs, 98.4749% and 98.4425%, was obtained for calibration and validation, respectively. The highest prediction accuracy was 99.5% and was obtained using principal component analysis-linear discriminant analysis (PCA-LDA). The best partial least square (PLS) calibration was obtained using the combined interval with R2cal = 0.898 and R2val = 0.874 for calibration and validation, respectively. In the prediction, the developed model could predict the adulteration level in the adulterated honey samples with an acceptable ratio of prediction to deviation (RPD) = 2.282, and range error ratio (RER) = 6.612.

A machine learning-based approach for predicting the level of palm oil adulteration in coconut oil

Coconut oil, prized for its health benefits, faces quality threats from adulteration, particularly with cheaper palm oil. This not only degrades the quality but also poses health risks. Traditional detection methods are often labor-intensive, destructive, and time-consuming. This study addresses the issue by applying multispectral imaging technology combined with machine learning to detect palm oil adulteration in coconut oil. We selected four machine learning algorithms—Support Vector Machines (SVM), Quadratic Discriminant Analysis (QDA), K-Nearest Neighbors (KNN), and Bagging—due to their robustness in handling complex datasets. These models achieved classification accuracies of up to 100 %, far surpassing traditional chemical tests, which are slower and dependent on tester expertise. To further enhance detection accuracy, we employed both hard- and soft-voting mechanisms, integrating the strengths of individual models to improve overall reliability. This research marks a significant advancement in detecting coconut oil adulteration, offering a faster, more efficient solution to ensure product quality and consumer health.

Design & development of adulteration detection system by fumigation method & machine learning techniques

A novel method for discovery of adulteration in edible oil is proposed based on concept of refractive index and electronic sensors. The research work focusses on two distinct methodologies like employing datasets and implementing a fumigation technique that integrates real-time hardware for testing Edible oil Impurities. In the first method, the dataset taken into consideration contains spectral data collected using Advanced ATR-MIR Spectroscopy for pure oil and various levels of adulteration with Vegetable oil. Each and every edible oil has a certain value of refractive index. When such oils are contemned in a change adding adulterants, the value of its refractive indices also changes. This value of refractive index serves as a feature for testing the oil and helps us in detecting the adulteration. If Oil is adulterated with vegetable oils, the refractive index will be lower and with animal fats, the refractive index will be higher than that of pure Oil. While in Fumigation Method a hardware module is develop in which adulterated & pure oil samples are heated at 40–50 °C for 4.66 min and the volatiles that are generated by varying gas concentrations are forcefully passed through to the MEMS Gas Sensor-MISC-2714 and Multichannel Gas sensor. The conductance of the sensors changes according to the gases sensed by the sensors contributes to features extraction. The conductance value serves as a feature for the classifier to determine whether the sample is highly, moderately, or lowly contaminated. Thus, in proposed methods we use different algorithms based on machine learning like KNN, Random Forest, CATBOOST and XGBOOST to accurately reveal the adulteration. Amongst all the applied algorithm Random Forest (RF) Classifier & XGBOOST algorithm outperform well and gives 100% accuracy. The proposed work is used for identifying food adulteration in edible food products which helps us to feed Society with high-quality food.

Visible feature engineering to detect fraud in black and red peppers

Visible imaging is a fast, cheap, and accurate technique in the assessment of food quality and safety. The technique was used in the present research to detect sea foam adulterant levels in black and red peppers. The fraud levels included 0, 5, 15, 30, and 50%. Sample preparation, image acquisition and preprocessing, and feature engineering (feature extraction, selection, and classification) were the conducted steps in the present research. The efficient features were classified using artificial neural networks and support vector machine methods. The classifiers were evaluated using the specificity, sensitivity, precision, and accuracy metrics. The artificial neural networks had better results than the support vector machine method for the classification of different adulterant levels in black pepper with the metrics’ values of 98.89, 95.67, 95.56, and 98.22%, respectively. Reversely, the support vector machine method had higher metrics’ values (99.46, 98.00, 97.78, and 99.11%, respectively) for red pepper. The results showed the ability of visible imaging and machine learning methods to detect fraud levels in black and red pepper.

Quantitative Analysis of High-Price Rice Adulteration Based on Near-Infrared Spectroscopy Combined with Chemometrics.

Near-infrared spectroscopy (NIRS) holds significant promise in detecting food adulteration due to its non-destructive, simple, and user-friendly properties. This study employed NIRS in conjunction with chemometrics to estimate the content of low-price rice flours (Nanjing, Songjing, Jiangxi silk, Yunhui) blended with high-price rice (Wuchang and Thai fragrant). Partial least squares regression (PLSR), support vector regression (SVR), and back-propagation neural network (BPNN) models were deployed to analyze the spectral data of adulterated samples and assess the degree of contamination. Various preprocessing techniques, parameter optimization strategies, and wavelength selection methods were employed to enhance model accuracy. With correlation coefficients exceeding 87%, the BPNN models exhibited high accuracy in estimating adulteration levels in high-price rice. The SPXY-SG-BPNN, SPXY-MMN-BPNN, KS-SNV-BPNN, and SPXY-SG-BPNN models showcased exceptional performance in discerning mixed Wuchang japonica, Thai fragrant indica, and Thai fragrant Yunhui rice. As shown above, NIRS demonstrated its potential as a rapid, non-destructive method for detecting low-price rice in premium rice blends. Future studies should be performed to concentrate on enhancing the models' versatility and practical applicability.

Molecular quantification of fritillariae cirrhosae bulbus and its adulterants

BackgroundFritillariae Cirrhosae Bulbus (FCB) is frequently adulterated with its closely related species due to personal or non-man made factors, leading to alterations in the composition of its constituents and compromising the efficacy of its products.MethodsThe specific single nucleotide polymorphisms (SNPs) were screened by comparing candidate barcodes of Fritillaria and verified by amplification and sequencing. Herb molecular quantification (Herb-Q) was established by detecting specific SNPs, and the methodological validation was performed. Quantitative standard curves were established for FCB mixed with each adulterated species, and the quantitative validity of this method was verified based on external standard substance. In addition, eight commercial Shedan Chuanbei capsules (SDCBs) randomly selected were detected.ResultsFCB and its five adulterants can be distinguished based on the ITS 341 site. The methodological investigation of Herb-Q shows optimal accuracy, and repeatability, which exhibited good linearity with an R2 of 0.9997 (> 0.99). An average bias in quantitative validity was 5.973% between the measured and actual values. Four of eight commercial SDCBs were adulterated with F. ussuriensis or F. thunbergia with adulteration levels ranging from 9 to 15% of the total weight.ConclusionThis study confirmed that Herb-Q can quantitatively detect both the mixed herbs and Chinese patent medicines (CPMs) containing FCB with high reproducibility and accuracy. This method provides technical support for market regulation and helps safeguard patient rights.

Fourier Transform Infrared Spectroscopy (FTIR) for identification of sugar adulterants in mulberry molasses (pekmez)

Fourier Transform Infrared Spectroscopy (FTIR) and chemometrics were successfully used in this research in conjunction with the Attenuated Total Reflectance (ATR) technique to determine bulberry molasses (MM) canesugar adulteration. Mulberry molasses was adultered by incorporating two fructose syrups (FS), one glucose syrup (GS), one sucrose syrup (SS), and two specific high fructose corn syrups (HFCS). Calibration sets were developed for mulberry molasses by spiking sugar syrups. The actual percentage of adulteration linked to FTIR predicted results by partial least square (PLS). The preferred spectral range was 1184-944 cm-1 for prediction of adulteration levels of F20, F30, HFCS50, HFCS55 and 1090-950 cm-1 for sucrose. This method was employed to distinguish between mulberry molasses and adulterated samples. According to the findings, the FTIR technique is an accurate and affordable method that can rapidly detect sugar adulteration in mulberry molasses.

Non-destructive detection of water adulteration level in fresh milk based on combination of dielectric spectrum technology and machine learning method

To rapidly realize the identification of fresh milk for water adulteration and to predict the amount of water adulteration, this study adopts a coaxial probe and a vector network analyzer to analyze the variation laws of dielectric constant ε', dielectric loss factor ε'' and dielectric loss angle tangent tanδ in the range of 2–20 GHz under 100 frequency points. Soft independent modeling of class analogy (SIMCA), Naive Bayes (NB), K-nearest neighbors (KNN), and support vector machine (SVM) models were built based on full dielectric spectra for qualitative testing of adulteration levels in milk. Feature variables of FDS were extracted by using the successive projections algorithm (SPA) and uninformative variables elimination (UVE). Partial least squares regression (PLSR), support vector regression (SVR), and particle swarm optimization least square support vector regression (PSO-LSSVR) models were built for quantitative testing of adulteration levels in milk. The results demonstrate an increasing trend of ε'' and tanδ with increasing frequency and a decreasing trend of ε'. The ε'-SIMCA model achieves the best effect in distinguishing water adulteration in milk, showing accuracy (ACC) of 1, Sensitivity (SNS) of 1, Specificity (SPC) precision (PRE) of 1, and an F1-score (F1) of 1. The tanδ-SPA-PSO-LSSVR model optimally predicts the optimal prediction effect of moisture content of milk, showing an RP2 of 0.994 and an RMSEP of 0.016 %. This study is conducive to building a more comprehensive knowledge system of water adulteration in fresh milk. Its results can provide a theoretical reference in the development of non-destructive detection instruments for natural milk.

Adulteration and Assessment of Raw Milk Quality in and Nearby Areas of Palampur Himachal Pradesh, India

Milk is one of the essential food for human intake, especially for growing children, which contains various proteins, fats, carbohydrates, vitamins and minerals. However, milk is prone to adulteration, minimizing its quality and safety even as it has numerous benefits to consumers. Present research monitored the physicochemical quality and the level of adulteration observed for the milk samples which were directly retrieved from the consumer’s door. The study established that there was variations in different studied parameters in 120 collected raw milk samples where CLR (27.16±5.15), fat (4.07±1.53), SNF (7.94±1.70), Total solid (12.00%), Protein (2.92±0.58%), Lactose (4.38±0.82), Minerals (0.65±0.12) were assessed. As for the intentionally added substances formalin, starch, cellulose, maltose and boric acid were not present, while ammonium sulphate (9.10%), urea (6.66%), neutralizers (2.50%), detergent (10%), sodium chloride (30%), skim milk powder (1.66%), sugar (2.50%), glucose (3.33%), hydrogen peroxide (45.83%) and pond water were found in different percentages. The investigation was carried out on the problem of adulteration faced and awareness regarding the quality of milk and its variations which was needed for better quality check system. The investigation made awareness amongst the society about the health dangers linked with the contaminated milk advancements in dairy equipment’s and farming techniques can also enhance the milk quality. Testing helped to measure the nutritional contents of milk including fats, vitamins and minerals. Study provided information about factors affecting milk quality and better storage conditions.

Polyphenolic profiling of coffee beverages by Liquid Chromatography-High-Resolution mass Spectrometry for classification and characterization

The importance of monitoring the presence of bioactive compounds as food attributes for sample classification and characterization is increasing. In this study, targeted Liquid Chromatography coupled with High-Resolution Mass Spectrometry (LC-HRMS) was employed to analyze the chemical profile of polyphenolic compounds as the source of information for the characterization and classification of 306 commercial coffee samples. Coffee holds a distinguished position as one of the most widely popular beverages globally but also one of the most easily adulterated. Regrettably, in recent times, instances of coffee adulteration have been on the rise. Consequently, implementing rigorous quality control measures for coffee becomes imperative to guarantee its quality. The results obtained in this work confirm that the proposed chemical profiles serve as excellent descriptors for sample characterization and classification through the implementation of principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA), achieving classification rates higher than 83.3% in PLS-DA validation. Moreover, the proposed LC-HRMS polyphenolic approach was employed to identify and measure adulteration levels in coffee samples using partial least squares (PLS) regression with prediction errors below 7.8%.

Untargeted authentication of fruit juices based on electrochemical fingerprints combined with chemometrics. Adulteration of orange juice as case of study

This work presents a novel strategy for authentication of fruit juices. The methodology is based on an electrochemical method combined with chemometrics. In addition, the case of orange juice adulteration with grapefruit juices was studied using this methodology. The electrochemical fingerprint of different juices showed the influence from different polyphenols according to the type of fruit. First, Principal Component Analysis (PCA) was able to differentiate clusters of different juices, being the higher group distance within apple, orange, and grape juices samples. On the other hand, partial least-squares – discriminant analysis (PLS-DA) and PCA-linear discriminant analysis (LDA) were used as classification methods, obtaining better results for orange and apple models. Furthermore, good results were obtained for the authentication of orange juices, compared to other juices, with an error rate of 0.04 for cross-validation. In the case of adulteration of orange juice was detected using PLS-DA at an adulteration level as low as 1%. Regression vectors for all models highlighted identifiable potential values that could be related to the main polyphenols in each type of fruit. This electrochemical method is rapid, low-cost, and compatible with on-site analysis compared to other laborious analysis described in the literature.

Identification and quantification of adulteration in collagen powder by terahertz spectroscopy − the effect of spectral characteristics on performance is considered

Terahertz spectroscopy is an emerging rapid detection method that can be used to detect and analyze food quality issues. However, models developed based on various spectral characteristics of terahertz have shown different performances in food identification. Therefore, we preliminarily analyzed the effect of terahertz spectral characteristics on the identification and quantification of collagen powder adulterated with food powders (plant protein powder, corn starch, wheat flour) with the use of random forest (RF), linear discriminant analysis (LDA), and partial least squares regression (PLSR), and determined the spectral characteristics suitable for identification and quantitative analysis. Then, the selected spectral characteristics data were preprocessed using baseline correction (BC), gaussian filter (GF), moving average (MA), and savitzky-golay (SG). Feature variables were extracted from preprocessed spectral characteristics data using genetic algorithm (GA), random forest (RF), and least angle regression (LAR). The study indicated that the BC-GA-LDA classification model based on the absorption coefficient spectra achieved an accuracy of 96.96% in identifying adulterated collagen powder. Additionally, the GA-PLSR model developed based on the power spectra demonstrated excellent performance in predicting adulteration levels, with the coefficient of determination (Rp2) values ranging from 0.93 to 0.99. The results showed that the rational selection of terahertz spectral characteristics is highly feasible for the accurate detection of collagen powder adulteration.

Identification of white wine adulteration with apple juice and apple cider using cyclic voltammetry on a screen-printed electrode aided by chemometric analysis

An electroanalytical approach has been developed for the identification and quantification of adulteration of white wine with apple juice and apple cider. After addition of LiClO4 as electrolyte and deoxygenation, the sample was analysed via cyclic voltammetry conducted on a screen-printed carbon electrode modified with gold nanoparticles. Cyclic voltammograms (CVs) of white wine samples displayed consistency regardless of their grape variety, mono-, bi- or multi-varietal status as well as geographical origin. In contrast, CVs of apple juice and apple cider exhibited similarities but were distinct from those of white wine. They were particularly characterized by the presence of a cathodic peak at about −0.50 V, attributed to sugars and organic acids, predominantly malic acid. Subsequently, cyclic voltammograms were exported into data points and they were subjected to chemometric analysis. Principal Component Analysis effectively grouped samples into two clusters: white wine and apple juice/ apple cider. Class-modelling demonstrated the ability to detect adulteration in white wine samples, with a detection threshold of 5 % v/v or lower, contingent upon the adulterant type (apple juice or apple cider). Partial Least Squares regression facilitated quantification of the adulteration level with acceptable accuracy. This approach is both cost-effective and straightforward, necessitating minimal sample preparation.

Identifying the degree of cornstarch adulteration in turmeric powder using optimized convolutional neural network

Adulteration in different spices is an emerging challenge in human civilization. It is commonly detected using different analytical and instrumental techniques. Despite good accuracy and precision many of such techniques are limited by their high processing time, skilled manpower requirement, expensive machinery and portability factor. Computer vision methodology driven by powerful convolutional neural network (CNN) architectures can be a possible way to address those limitations. This paper presents a CNN driven computer vision model which can detect cornstarch adulteration in turmeric powder along with the degree of adulteration. The model has been optimized using binary genetic algorithm (BGA) for improved performance and consistency. The experimentations presented in this paper were conducted with an in-house database prepared for 4 levels of adulteration and found to provide about 98% overall accuracy. The less expensive and faster detection capability of the model along with its mobility makes this proposal a promising addition to the existing spice adulteration screening methods.

Adulteration detection of multi-species vegetable oils in camellia oil using Raman spectroscopy: Comparison of chemometrics and deep learning methods

Oil adulteration is a global challenge in the production of high value-added natural oils. Raman spectroscopy combined with mathematical modeling can be used for adulteration detection of camellia oil (CAO). In this study, the advantages of traditional chemometrics and deep learning methods in identifying and quantifying adulterated CAO were compared from a statistical perspective, and no significant difference were founded in the identification of CAO at different levels of adulteration. The recognition rate of pure and adulterated CAO was 100 %, but there were misclassifications among different adulterated CAOs. The deep learning models outperformed chemometrics methods in quantitative prediction of adulteration level, with RP2, RMSEP, and RPD of the optimal ConvLSTM model achieved 0.999, 0.9 % and 31.5, respectively. The classifiers and models developed in this study based on deep learning have wide applicability and reliability, and provide a fast and accurate method for adulteration detection in CAO.

Classification of wheat flour levels in powdered spices using visual imaging

The present research aims to investigate the ability of image processing in the detection of wheat flour adulterant in different spices. Different samples of black pepper, red pepper, and cinnamon were prepared with different levels of wheat flour adulteration. The images of the samples were captured using a visible camera. An image processing algorithm was designed and codded in MATLAB software to process the acquired images. Different color and textural features were extracted. The efficient features were selected and classified using artificial neural network and support vector machine. In the present research, the classification accuracies of artificial neural network analysis for black pepper, red pepper, and cinnamon products were 97.8, 100.00, and 100.00 %, respectively. During the experiment, these results were better than the performance of the support vector machine, (93.33, 100.00, and 98.88 %, respectively). Visible image processing can be used accurately to detect different adulterant levels that increase product quality while reducing operating costs.

Comparison of strength and adulteration between illicit drugs obtained from cryptomarkets versus off-line.

Drugs sold on cryptomarkets are thought to have lower levels of adulteration and higher strength compared with those sourced off-line. The present study aimed to determine whether cryptomarket and off-line-sourced 3,4-methylenedioxy-N-methamphetamine (MDMA), cocaine, amphetamine, methamphetamine and lysergic acid diethylamide (LSD) differed in adulteration and strength. A between-groups design was used to compare cryptomarket versus off-line-sourced drugs. Regression analyses controlling for year and service were conducted. Drug-checking services were conducted in Spain (Energy Control) and the Netherlands (Drugs Information and Monitoring System). The cases comprised drug samples that underwent drug checking between 2016 and 2021 and were expected to contain MDMA (tablets; n = 36 065; powder: n = 6179), cocaine (n = 11 419), amphetamine (n = 6823), methamphetamine (n = 293) and LSD (n = 1817). Drugs were measured for (1) matching the advertised substance (i.e. containing any amount of the expected substance); (2) strength; (3) presence of adulteration; and (4) number of adulterants. The expected drug was more likely to be identified when sourced from cryptomarkets versus off-line for MDMA tablets [adjusted odds ratio (AOR) = 2.10, 95% confidence interval (CI) = 1.28-3.43], MDMA powder (AOR = 2.64, CI = 1.55-4.51), cocaine (AOR = 3.65, CI = 1.98-6.71) and LSD (AOR = 1.75, CI = 1.13-2.72). Cryptomarket-sourced MDMA powder (β = 0.03, P = 0.012), cocaine (β = 0.08, P < 0.001) and methamphetamine (β = 0.15, P = 0.028) were statistically significantly higher in strength than substances from off-line sources. Conversely, MDMA tablets (β = -0.01, P = 0.043) and amphetamine (β = -0.07, P < 0.001) from cryptomarkets were statistically significantly lower in strength than from off-line sources. MDMA powder (AOR = 0.53, CI = 0.33-0.86) and cocaine (AOR = 0.66, CI = 0.55-0.79) were statistically significantly less likely to be adulterated if sourced from cryptomarkets. However, amphetamine (AOR = 1.54, CI = 1.25-1.90) and LSD (AOR = 1.31, CI = 1.00-1.71) were found to be more likely to be adulterated when purchased from cryptomarkets. Cocaine from cryptomarkets exhibited fewer adulterants (incidence rate ratio = 0.71, CI = 0.60-0.85). The relationship between on-line drug market-places and substance quality varies depending on both the specific substance and the dynamics of the cryptomarket.

Exploring the use of portable infrared spectroscopy as a quality assurance tool for the Australian Native/Traditional food industry: a case study on finger lime (Citrus australasica)

SummaryFinger limes (Citrus australasica) are an Australian native and traditional food that are known for their caviar‐like pearls often referred to as “Citrus Caviar”. This study aimed to spectrally characterise finger lime varieties sourced across South‐East Queensland and Northern New South Wales. The varieties investigated demonstrated a wide diversity in their phenotypic appearance; however, their infrared spectra were similar. The NIR spectra of the samples were collected using a portable instrument and the data analysed using principal component analysis (PCA) and partial least squares (PLS) regression. Differences in the PCA score plot were observed associated with the different varieties analysed. The cross‐validation statistics obtained for the prediction of adulteration of finger with Tahitian lime were R2CV and SECV 0.96% and 7%, respectively (using 8 PLS factors), using PLS regression. This study demonstrated that a portable handheld NIR instrument is able to identify finger lime varieties using both whole fruits and the corresponding peel samples. This study also showed that NIR was able to identify the level of adulteration of finger lime with Tahitian lime.

Application of handheld near infrared spectrometer in quality control of traditional Chinese medicine: Rapid screening and quantitative analysis of Lonicerae Japonicae Flos adulteration

Traditional Chinese medicine (TCM) prescription, with its intricate formulations and nuanced compositions, is a cornerstone of holistic health practices. However, the expansion of the TCM market has led to a surge in herb adulteration, which significantly undermines the quality and safety of these medicinal products. A case in point is Lonicerae Japonicae Flos (LJF), a widely utilized herb for treating colds, which has been adulterated by the cheaper Lonicerae Flos (LF), thereby affecting its therapeutic effectiveness. Therefore, a method utilizing handheld NIR spectroscopy combined with chemometrics has been developed to provide a portable, real-time solution for the rapid and accurate detection and quantification of adulterants in TCM. By collecting NIR spectra from LJF, LF and adulterated samples (AS), we’ve established a spectral database enabling deep insights into the correlation between spectral features and sample compositions. Resultantly, a classification model with a 99.58 % cross-validation accuracy, reaching 100 % for test set, effectively identified adulterants. And further spectral similarity analysis and classification identification of samples with different adulteration ratios were carried out. The cross-validation accuracy under the optimal model reached 98.38 %, and the test set accuracy was 99.20 %. In addition, the study extends to quantifying different levels of adulteration, employing 20 standard adulterated samples across a 0–100 % adulteration gradient. Via data preprocessing, feature extraction, and regression techniques, the full concentration prediction models were developed, later refined by segmenting samples based on high and low adulteration ratios. Under the SGFD_CARS_PLS (Savitzky-Golay smoothing with the first derivative_competitive adaptive reweighted sampling_partial least squares) model, exceptional performance was achieved, with a R2p of 0.983, RMSEp of 3.402, and RPDp of 7.757 for the homemade adulterated prediction set. In conclusion, the application of this technology not only improves the efficiency and accuracy of screening, but also has the advantages of low cost, easy operation and rapid results compared with traditional chemical analysis methods. It effectively protects the safety of drugs for consumers, maintains the integrity of the TCM market, and provides a strong technical support for the on-site rapid detection of TCM.