Analysis Of Adulteration Research Articles

Comprehensive Analysis of Preservative Levels and Adulteration in Egyptian Market Cake Samples

Comprehensive Analysis of Preservative Levels and Adulteration in Egyptian Market Cake Samples

Analysis of species adulteration in beef sausage using real-time polymerase chain reaction in Makassar, Indonesia

Background and Aim: Adulteration, or the inclusion of meats not declared on the label of processed meat products, constitutes a fraudulent practice that poses a threat to public health. Sausages, which are processed meats derived from a blend of minced meats that obscure the original muscle’s morphological features, are particularly prone to adulteration, making the visual detection of fraud more challenging. The research aimed to detect and measure the proportion of pork, chicken, buffalo, and beef DNA in commercially available processed meat packaged, labeled, and sold as “beef sausages” in Makassar, Indonesia. Materials and Methods: A total of 30 beef sausage samples were collected from traditional and modern markets as well as tourist attractions in Makassar. DNA was isolated and the species were identified using quantitative polymerase chain reaction. Results: The findings revealed that all sausage samples contained not only beef DNA, as indicated on their labels but also undeclared DNA from chicken and buffalo. Notably, pork DNA was not detected in the samples. The frequencies of chicken and buffalo meat were 9.2% and 10%, respectively, whereas it was 0.85% for beef in the beef sausage samples. Conclusion: The discovery of chicken and buffalo species in beef sausage samples indicates adulteration, potentially posing severe quality risks. Keywords: adulteration, beef sausage, deoxyribonucleic acid, polymerase chain reaction, species.

Digital identification and adulteration analysis of bupleurum chinense and bupleurum marginatum based on "digital identity" and UHPLC-QTOF-MSE

Background and objectivesone of the legal sources of Bupleuri Radix (BR) is the Bupleurum chinense (BC), rather than the Bupleurum marginatum (BM). However, fake incidents of BM mixed with BC often occur in the market, which makes it more difficult to supervise the quality of BR and even endanger the life and health of patients. To strengthen the quality control and supervision of BP, we carried out digital identification and adulteration analysis of BC and BM based on the "digital identity" and UHPLC-QTOF-MSE. Methodfirstly, the BC and BM were analyzed by UHPLC-QTOF-MSE to obtain the quantized data characterization of chemical components. Secondly, the shared ions were extracted from different batches of BC and BM's control medicinal materials as their "data representation of ions", respectively. Then, the data matrices of unique ions of BC relative to BM and BM relative to BC were screened out, and the Top-N ions were outputted as the "digital identities" of BC and BM, sorted by ionic strength. Finally, the above "digital identities" of BC and BM were used as benchmarks for matching positive samples and commercial samples to feedback on matching credibility (MC). Resultsthe results showed that based on the "digital identities" of BC and BM, the digital identification of BC, BM, and positive samples can be realized efficiently and accurately at the individual level of Chinese medicine, even if 3 % of BM in the mixed samples can still be identified efficiently and accurately. 10 batches of market samples were identified as adulterated samples. Furthermore, chemometric analysis has proven the reliability of BC and BM-based "digital identity" identification. ConclusionIt proved that the identification and adulteration analysis of two herbs can be realized efficiently and quickly through the "digital identities" of BC and BM. It has important reference significance for developing non-targeted digital identification of herbal medicines at the individual level of Chinese medicine based on "digital identity", which was beneficial to the construction of digital Chinese medicine and digital quality control.

The use of PCR method for adulteration detection of goat dairy products manufactured by smallholders

The analysis of food adulteration is an important element of improving health safety. Both accidental and intentional adulteration of food, can cause serious health consequences for humans and can be considered as health hazard. In recent years, molecular biology techniques has been increasingly used in food analysis due to their high precision and the ability to obtain results in a short time, as well as the identification and quantitative determination of food adulterants. The samples tested in the study were goat's milk, yogurts, fresh and ripening cheeses produced by smallholders all over Poland. The analysis included DNA isolation, which was used as template for duplex PCR and detection of goat’s and cow’s mitochondrial DNA fragments. The obtained results indicate that the applied technique enabled the rapid identification of adulteration in analyzed goat’s milk products, among which in only one product out of thirty five the presence of undeclared cow’s milk was detected. This indicates good food safety standards applied on small-scale farms.

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.

Digital identification and adulteration analysis of Pulsatilla Radix and Pulsatilla Cernua based on “digital identity” and UHPLC-QTOF-MSE

Under the background of digitalization of traditional Chinese medicine (TCM), to realize the quick identification and adulteration analysis of Pulsatilla Radix (PR), adhering to digital conviction, this study conducted UHPLC-QTOF-MSE analysis on PR and its adulterant-Pulsatilla Cernua (PC) from different batches and based on digital conversion, the shared ions were extracted from different batches of PR and PC as their “ions representation”, respectively. Further, the data set of unique ions of PR relative to PC and PC relative to PR were screened out as the “digital identities” of PR and PC respectively. Further, above the “digital identities” of PR and PC were used as the benchmarks for matching and identifying to feedback give a matching credibility (MC). The results showed that based on the “digital identities” of PR and PC, the digital identification of two herbal samples can be realized efficiently and accurately at the individual level with the MC≥70.00 %, even if 5 % of PC in the mixed samples can still be identified efficiently and accurately. The study is of great practical significance for improving the identification efficiency of PR and PC, cracking down on adulterated and counterfeit drugs, and strengthening the quality control of PR. In addition, it has important reference significance for developing non-targeted digital identification of herbal medicines at the individual level based on UHPLC-QTOF-MSE and the “digital identity”, which was beneficial to the construction of digital Chinese medicine and digital quality control.

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

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. 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. 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. 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.

Analysis of milk adulteration by means of a potentiometric electronic tongue

The adulteration of milk presents significant challenges in the food industry, promoting the need for efficient detection methods. This study introduces a potentiometric electronic tongue for rapid and accurate milk adulteration detection. Utilizing polymeric membranes integrated with various additives, the electronic tongue distinguishes between different milk types and detects common adulterants. Experimental results demonstrate its effectiveness in discriminating raw, pasteurized, and medicated cow milk, as well as goat milk. Moreover, it successfully identifies adulterants like water and bovine milk in goat milk samples. Chemometric analyses, including Principal Component Analysis and Partial Least Squares regression, correlate sensor responses with traditional milk parameters such as fat, protein, and lactose content with up to a 0.97 R2 on the validation step. Strong correlations validate the electronic tongue's potential for rapid milk quality assessment. This innovative approach offers a cost-effective, reliable solution for detecting milk adulteration in contrast with current techniques that require numerous, time consuming experiments.

Adulteration of Brain Health (Cognitive, Mood, and Sleep Enhancement) Food Supplements by the Addition of Pharmaceutical Drugs: A Comprehensive Review of Analytical Approaches and Trends.

In recent years, the consumption of dietary supplements has grown worldwide, particularly in developed regions. However, this growing market has also become a prime target for adulteration practices, with some manufacturers illegally adding pharmaceuticals into plant-based food supplements (PFS) to enhance their effects. While extensive research has focused on detecting adulterant drugs in PFS tailored for improving sexual performance, weight loss, and muscle building, less attention has been given to supplements intended for mood enhancement, sleep aid, and cognitive function (nootropics). Nonetheless, recent reports indicate an increasing level of adulteration within this group of PFS. Therefore, this review aims at providing a comprehensive overview on the adulteration of PFS tailored for brain health, with a focus on the analytical techniques utilized for detection while also presenting data on consumption patterns and the prevalence of reported adulterants. Considering that the detection of such fraudulent practices primarily relies on chromatographic techniques coupled with mass spectrometry (MS), the developments in this field comprising either targeted or untargeted analysis of pharmaceutical adulterants are discussed.

Adulteration Detection and Quantification in Olive Oil Using Excitation-Emission Matrix Fluorescence Spectroscopy and Chemometrics.

This research investigates the use of excitation-emission matrix fluorescence (EEMF) in conjunction with chemometric models to rapidly identify and quantify adulteration in olive oil, a critical concern where sample availability is limited. Adulteration is simulated by blending soybean, peanut, and linseed oils into olive oil, creating diverse adulterated samples. Principal component analysis (PCA) was applied to the EEMF spectral data as an initial exploratory measure to cluster and differentiate adulterated samples. Spatial clustering enabled vivid visualization of the variations and trends in the spectra. The novel application of parallel factor analysis (PARAFAC) for data decomposition in this paper focuses on unraveling correlations between the decomposed components and the actual adulterated components, which offers a novel perspective for accurately quantifying adulteration levels. Additionally, a comparative analysis was conducted between the PCA and PARAFAC methodologies. Our study not only unveils a new avenue for the quantitative analysis of adulterants in olive oil through spectral detection but also highlights the potential for applying these insights in practical, real-world scenarios, thereby enhancing detection capabilities for various edible oil samples. This promises to improve the detection of adulteration across a range of edible oil samples, offering significant contributions to food safety and quality assurance.

Real-Time Authentication of Camellia Oil by Rapid Evaporative Ionization Mass Spectrometry

Camellia oil is a high-value product with rich nutrients. Recently, the adulteration of camellia oil has become an increasingly concerning issue related to human health. In this study, electric soldering iron coupled with rapid evaporative ionization mass spectrometry (REIMS) was employed for the identification and analysis of camellia oil without any sample preparation. REIMS technology coupled with chemometrics was applied to develop an analysis model for the authentication of camellia oil adulterated with soybean oil, peanut oil, rapeseed oil, sunflower oil, and corn oil (5–40%, v/v). The results showed that different types of vegetable oils could be classified using principal component analysis-linear discriminant analysis (PCA-LDA) with a correct classification of 93.8% in leave-20%-out cross-validation and 100% correctly identified in real-time recognition. The established prediction models were found to be particularly sensitive when the camellia oil samples were adulterated with 5–40% of other oils, indicating that REIMS could be a powerful tool for the authentication and adulteration analysis of camellia oil, particularly for cases where the adulteration levels are relatively high. In conclusion, the results provide valuable insights into the potential of REIMS for the rapid, accurate, and real-time authentication and adulteration analysis of camellia oil.

Amplification-free quantitative detection of genomic DNA using lateral flow strips for milk authentication

With the globalization and complexity of the food supply chain, the market is becoming increasingly competitive and food fraudulent activities are intensifying. The current state of food detection faced two primary challenges. Firstly, existing testing methods were predominantly laboratory-based, requiring complex procedures and precision instruments. Secondly, there was a lack of accurate and efficient quantitative detection methods. Taking cow's milk as an example, this study introduced a novel method for nucleic acid quantification in dairy products, based on lateral flow strips (LFS). The core idea of this method is to design single-stranded DNA (ssDNA) probes to hybridize with mitochondrial genes, which are abundant, stable, and species-specific in dairy products, as detection targets. Drawing inspiration from the principles of nucleic acid amplification, this research innovatively established a new DNA hybridization method, named LAMP-Like Hybridization (HybLAMP-Like). Leveraging the denaturation and DNA polymerization functions of the bst enzyme, efficient binding of the probe and template strand was achieved. This method eliminated the need for nucleic acid amplification, simplifying the procedure and mitigating aerosol contamination, thereby ensuring the accuracy of the detection results. The method exhibited exceptional sensitivity, capable of detecting extremely low to 12.5 ng in visual inspection and 3.125 ng when using a reader. In terms of practicality, it could achieve visual detection of cow's milk content as low as 1% in adulterated dairy products. When combined with a portable LFS reader, it also enabled precise quantitative analysis of milk adulteration.

The identification of aged-rice adulteration by support vector machine classification combined with characteristic wavelength variables

The aging of rice during long-term storage leads to a decline in quality and a decrease in consumption value. Therefore, the efficient and rapid detection of aged-rice adulteration is crucial for rice quality control. This paper aims to utilize near-infrared spectroscopy (NIRS) in conjunction with chemometrics to identify two types of rice adulteration: Late japonica rice and Southern japonica rice. To achieve this, competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) were utilized to select characteristic wavelengths from the spectral data, both with and without pretreatment methods. In order to establish a support vector machine classification (SVC) model with exceptional predictive capability, cross validation (CV), genetic algorithm (GA), and particle swarm optimization (PSO) were adopted to find the best combination of parameters (C, g). Notably, the prediction accuracy achieved by MMN-CARS-CV-SVC, SNV-CARS-CV-SVC, SNV-CARS-GA-SVC, and SNV-CARS-PSO-SVC models was 98% for Southern japonica rice adulteration, with varying proportions of aging rice. Conversely, the prediction accuracy of the MSC-CARS-CV-SVC model reached 96% for Late japonica rice adulteration, with varying proportions of aged rice. In conclusion, the combination of NIRS and SVC enhances the discriminative analysis of aged-rice adulteration, and the outstanding generalization ability of the SVC model offers new possibilities for the analysis of adulteration in other cereal grains.

Analysis of microbiological quality and adulteration of raw milk samples from different areas of Kathmandu Valley

Milk is a highly nutritious product that is susceptible to degradation due to microbial activity. Maintaining milk quality is crucial and can be achieved by monitoring specific parameters. This helps preserve the nutritive value of milk, which is essential for proper growth and health. Adulteration and improper storage can diminish the nutritional quality of milk. Therefore, this study aimed to assess the microbial load and adulteration of milk samples collected from various regions of the Kathmandu Valley. Sixty raw milk samples were gathered from local dairies (45) and cow farms (15) between April 2019 and July 2019. These samples were evaluated for microbial quality (total plate count, total coliform count, Salmonella spp., Shigella spp., and Vibrio spp.) and adulterants (starch, table sugar, soda, soap, and hydrogen peroxide) following standard guidelines. Out of the total samples, 58.3% (35) exhibited coliform growth, while Shigella spp. and Vibrio spp. did not grow on any media. Among coliforms, Enterobacter spp. was the most prevalent at 33.3%, followed by Escherichia coli at 32%. Antibiotic susceptibility testing revealed that the highest proportion of bacteria was sensitive to Ciprofloxacin and Gentamycin, followed by Ceftazidime. Adulteration analysis indicated that 33.3% and 48.3% of samples were adulterated with sugar and soda, respectively. Starch and soap were not detected in any analyzed samples. The highest titratable acidity (0.16%) was observed in cow farms compared to dairy farms. The findings of this study suggest an urgent need for routine quality testing of milk samples available in the market to prevent the spread of milk-borne diseases and preserve the nutritive value of milk.

Inhibiting Protein Aggregation Using Cellulose Nanocrystal in MALDI-TOF MS Analysis: Improving the Sensitivity and Repeatability of Intact Protein in Pueraria.

Protein aggregation can induce low sensitivity and poor repeatability of matrix-assisted laser desorption/ionization time-of-fight mass spectrometry (MALDI-TOF MS) analysis for intact protein. Herein, we introduced a strategy to decrease protein aggregation in the sample solution by using cellulose nanocrystal (CNC). The results indicated that protein granule size was effectively reduced by adding CNC to the sample solution. Through MALDI-TOF MS analysis, the signal-to-noise ratio of [M + H]+ peak increased 2-fold, and the detection of limit was <10 μg/mL for intact protein. The CNC also contributed to excellent point-to-point repeatability for MALDI-TOF MS analysis with the coefficient of variation (CV) of 10.0% with CNC vs 48.9% without CNC in Hb solution. Also, the repeatability of Pueraria protein ion signals was improved by using CNC, and the CV with and without CNC was 16.1% and 39.6%, respectively. Moreover, protein ion intensity exhibited great linear relationship (y = 53.04x - 3.474, R2 = 0.9936) with the concentrations (ranging from 0.1 to 10 mg/mL) when using CNC. Further investigation revealed that m/z 19,000 and m/z 21,000 peaks of Pueraria could be used for the adulteration analysis and post-translational modification research, demonstrating our method has the potential for broad applications.

Adulteration: Supply of Raw Milk and Prevalence of Adulterated / Prepared Milk

Raw milk adulteration is one of the food fraud to gain financial benefits by removing fat and cream from whole milk and compensating it by addition of different adulterants to make it near to wholesome milk. Objectives: To analyze milk adulterants to not only describe about the prevalence of different milk adulterants but also confirm the sources of adulterants being used for the synthesis of semi synthetic or prepared milk due to their characteristics which they impart to form a sort of wholesome milk. Methods: In this regard total of 190 raw milk samples from cow and buffalo sources along with control and in house standards were taken to detect most prevailing adulterants in raw milk samples through automated and/or titration based manual recommended methods. Results: The analysis of different adulterants in milk samples showed water in 148(77.89%) raw milk samples, Detergent in 62(32.9%), Cane Sugar 41(21.8%), Caustic Soda 32(16.8%), Sodium Salts 31(16.4%), Starch 21(11.1%), Formalin 18(9.4%), Urea 15(8.05%), Foreign Fat 12(6.4%), Hydrogen Peroxide 04(2.3%), Glucose 02(1.3%), Boric Acid 02(1.1%) and sulfate salts 02(1.1%) in raw milk samples. Conclusions: The assessment of quality of fresh milk showed poor quality milk with the provision of different sort of adulterants in 77.89% raw milk samples and alarming sign of semi synthetic milk in 2.63% samples.

Rapid detection and quantification of adulteration in saffron by excitation-emission matrix fluorescence combined with multi-way chemometrics.

Saffron has gained people's attention and love for its unique flavor and valuable edible value, but the problem of saffron adulteration in the market is serious. It is urgent for us to find a simple and rapid identification and quantitative estimation of adulteration in saffron. Therefore, excitation-emission matrix (EEM) fluorescence combined with multi-way chemometrics was proposed for the detection and quantification of adulteration in saffron. The fluorescence composition analysis of saffron and saffron adulterants (safflower, marigold and madder) were accomplished by alternating trilinear decomposition (ATLD) algorithm. ATLD and two-dimensional principal component analysis combined with k-nearest neighbor (ATLD-kNN and 2DPCA-kNN) and ATLD combined with data-driven soft independent modeling of class analogies (ATLD-DD-SIMCA) were applied to rapid detection of adulteration in saffron. 2DPCA-kNN and ATLD-DD-SIMCA methods were adopted for the classification of chemical EEM data, first with 100% correct classification rate. The content of adulteration of adulterated saffron was predicted by the N-way partial least squares regression (N-PLS) algorithm. In addition, new samples were correctly classified and the adulteration level in adulterated saffron was estimated semi-quantitatively, which verifies the reliability of these models. ATLD-DD-SIMCA and 2DPCA-kNN are recommended methods for the classification of pure saffron and adulterated saffron. The N-PLS algorithm shows potential in prediction of adulteration levels. These methods are expected to solve more complex problems in food authenticity. © 2023 Society of Chemical Industry.

A rapid and low-cost method for detection of nine kinds of vegetable oil adulteration based on 3-D fluorescence spectroscopy

The integrity of vegetable oil has become a focal concern within the global food industry, particularly in specific regions where economic interests drive authenticity issues. In this study, we used three-dimensional (3-D) fluorescence spectroscopy for the qualitative and quantitative analysis of adulteration in different vegetable oils, offering the advantages of rapid and low-cost detection. Nine types of adulterated vegetable oils were modeled using five machine learning methods: K-nearest neighbor (KNN), Random Forest (RF), Support Vector Machine (SVM), Partial Least Squares (PLS), and Convolutional Neural Network (CNN). In the qualitative analysis, the Rp2 and RMSEP values ranged from 0.89 to 0.99 and 0.01 to 0.05, respectively. For qualitatively analysis, a threshold of 5 % was established for detecting adulteration in soybean oil with palm oil and sesame oil with palm oil, achieving an accuracy (ACC) greater than 90 %. Meanwhile, a threshold of 10 % was effective for remaining seven adulterated oils, with an ACC exceeding 90 %. Moreover, an ACC rate of over 95 % was attained when the threshold was set at 15 %. The findings demonstrate that 3-D fluorescence spectroscopy presents a robust tool for detecting various adulterated oils, heralding a valuable contribution to food safety and quality control.

Authentication analysis of milkfish oil treated with activated charcoal and bentonite using Fourier Transform Infrared Spectroscopy and Multivariate Data Analysis

Adulteration issues are important not only for fats and oils consumers but also for producers and regulatory bodies, therefore authentication analytical methods are continuously developed. The objective of this study was to highlight the employment of attenuated total Fourier transform infrared (FTIR) spectroscopy and chemometrics for the authentication of milkfish oil (MFO) from pumpkin seed oil (PSO). In this study, ATRFTIR spectra of MFO, PSO and its binary mixtures were scanned, and the information obtained was used for quantitative analysis of adulterants and classification between authentic MFO and MFO adulterated with PSO. Two multivariate calibrations of partial least square calibration (PLSR) and principal component regression (PCR) were compared for their performance during quantitative analysis and linear discriminant analysis was applied for discrimination between authentic MFO and MFO adulterated with PSO. The results revealed that PLSR using 2nd derivative spectra at wavenumbers of 1500-1000 cm-1 was preferred for simultaneous quantitative analysis of MFO-treated with activated charcoal, while PCR using 2nd derivative spectra at wavenumbers of 1500-1000 cm-1 was suitable for analysis of MFO-treated with bentonite providing the acceptable values of R2 , root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP). Linear discriminant Analysis using normal FTIR spectra at the whole wavenumber region was successful for the discrimination of MFO treated with activated charcoal and bentonite with accuracy levels of 100%. It can be concluded that the chemometrics approach for ATR-FTIR spectra could be a simple and effective tool for the authentication of MFO.

Rapid quantitative analysis of Rongalite adulteration in rice flour using autoencoder and residual-based model associated with portable Raman spectroscopy

Rice flour is a raw material for various foods and is used as a substitute for wheat flour. However, some merchants adulterate rice flour with the illegal additive Rongalite to extend the shelf life and earn illegal profits. Rongalite is highly carcinogenic, and ingestion of more than 10 g can even cause death. high-performance liquid chromatography (HPLC) and mass spectrometry (MS) are currently the main methods for detecting food adulteration, however, the existing methods have many limitations, complex operation, expensive instrumentation, etc. Raman spectroscopy has the advantages of convenience and non-destructive samples, but Raman spectroscopy can be affected by interference such as fluorescence background that affects detection, in addition to the problem of difficult quantitative analysis due to nonlinear bias. In this article, we used the preprocessing method of Savitzky-Golay smoothing filtering and VTPspline to improve the quality of the spectra and proposed the SARNet, which combines autoencoder and residual network to achieve the quantitative analysis of Rongalite content in rice flour. The new model combines a linear model with a nonlinear model, which can solve the nonlinear problem effectively. Experiments showed that the new SARNet model achieved state-of-the-art results, achieving the best R2 of 0.9703 and RMSEP of 0.0075. The lowest Rongalite concentration detected by the portable Raman spectrometer was 0.49%. In summary, the proposed method using portable Raman spectroscopy combined with machine learning has low detection bias and high accuracy, which can realize quantitative analyses of adulterated Rongalite in rice flour quickly. The method provides an accurate and nondestructive analytical tool in the field of food detection.