Rapid Authentication of 100% Italian Durum Wheat Pasta by FT-NIR Spectroscopy Combined with Chemometric Tools

Girolamo, Annalisa De; Cervellieri, Salvatore; Mancini, Erminia; Pascale, Michelangelo; Logrieco, Antonio F.; Lippolis, Vincenzo

doi:10.3390/foods9111551

Cited by 13 publications

(10 citation statements)

References 35 publications

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“…Metabolic fingerprinting is used without preliminary identification of individual metabolites [11]. De Girolamo [12] simplifies the strategy to determine the authentification product become two strategies, targeted and untargeted analytical approaches. Targeted analytical approaches are specific to adulterant and their quantification, e.g., LC-MS and GC-MS. Untargeted analytical offer minimal information about the type of adulterant and focus on the classification of unknown products as belonging to the class of authentic product or not, e.g., NIR, MR, and Raman spectroscopy [13].…”

Section: Techniques To Discriminate Product Authenticitymentioning

confidence: 99%

Discriminate geographical origin of cabe jamu (Piper retrofractum vahl) using nir spectral fingerprints– a short review

Mu'tamar

Hidayat

Mahardika

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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Cabe jamu is categorized as the primary material for the jamu industry because it contains an active compound that provides a health benefit. Furthermore, some area in east java is known as the central production of cabe jamu. Almost 80% of the plant area is located at Madura Island, covering Bangkalan, Sampang, Pamekasan, and Sumenep. Agricultural products have different characteristics when planted at different locations because of climate, soil, and environmental condition. However, counterfeit practice deteriorates the specialty of products for economic purposes. Several techniques were developed detection system of products based on geographical origin or authenticity. This paper aims to explore potential technology that is able to evaluate the authentication of products and potentially applied to cabe jamu. Overall, several strategies were obtained from this literature study to provide the high-accuracy classification of cabe jamu related to geographical origin: Coupling NIR spectroscopy with chemometrics, utilization of multiple techniques like other vibrational spectroscopy or non-vibrational spectroscopy, and use of data fusion approach to elevate the accuracy of the classification model.

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Section: Techniques To Discriminate Product Authenticitymentioning

confidence: 99%

Discriminate geographical origin of cabe jamu (Piper retrofractum vahl) using nir spectral fingerprints– a short review

Mu'tamar

Hidayat

Mahardika

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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“…Dilek et al [ 44 ] used the FTIR method to detect fumonisins due to Fusarium niger contaminated raisins and demonstrated that Fusarium niger contaminated raisins showed an increased absorbance in the characteristic bands at 1733 cm −1 , 1736 cm −1 , and 1708 cm −1 , demonstrating that the FTIR spectroscopy technique is capable of determining fumonisins and other mycotoxins in agricultural products. Annalisa et al [ 45 ] used Fourier transform near-infrared (FT-NIR) and Fourier transform mid-infrared (FT-MIR) spectra combined with chemometrics for the first time to detect deoxynivalenol (DON) in wheat bran samples. Standard normal variables (SNV) transformation, mean normalization, and other preprocessing methods were used to compare and fuse the processed spectral data.…”

Section: Application Of Infrared Spectroscopy Technology In Detection...mentioning

confidence: 99%

Research Progress of Applying Infrared Spectroscopy Technology for Detection of Toxic and Harmful Substances in Food

Tian

et al. 2022

Foods

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In recent years, food safety incidents have been frequently reported. Food or raw materials themselves contain substances that may endanger human health and are called toxic and harmful substances in food, which can be divided into endogenous, exogenous toxic, and harmful substances and biological toxins. Therefore, realizing the rapid, efficient, and nondestructive testing of toxic and harmful substances in food is of great significance to ensure food safety and improve the ability of food safety supervision. Among the nondestructive detection methods, infrared spectroscopy technology has become a powerful solution for detecting toxic and harmful substances in food with its high efficiency, speed, easy operation, and low costs, while requiring less sample size and is nondestructive, and has been widely used in many fields. In this review, the concept and principle of IR spectroscopy in food are briefly introduced, including NIR and FTIR. Then, the main progress and contribution of IR spectroscopy are summarized, including the model’s establishment, technical application, and spectral optimization in grain, fruits, vegetables, and beverages. Moreover, the limitations and development prospects of detection are discussed. It is anticipated that infrared spectroscopy technology, in combination with other advanced technologies, will be widely used in the whole food safety field.

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“…True positives True positives + False positives (5) Recall = True positives True positives + False Negatives ( 6) A Vis-NIR Spectroscopy was used to determine how millet varieties differ in how some incident radiation is reflected, transmitted, and absorbed. As presented in Figure 1, the emitted energy produced a series of stripes and a few peaks.…”

Section: Precision =mentioning

confidence: 99%

“…The food traceability system, which the European Union officially recognized (E.U.) in 2000, is an essential tool for protecting consumers against contamination [5]. Keeping foods free of contamination and illustrating the complete identification of products receives significant attention in many countries.…”

Section: Introductionmentioning

confidence: 99%

Geographic Origin Discrimination of Millet Using Vis-NIR Spectroscopy Combined with Machine Learning Techniques

Kabir

Guindo

Chen

et al. 2021

Foods

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Millet is a primary food for people living in the dry and semi-dry regions and is dispersed within most parts of Europe, Africa, and Asian countries. As part of the European Union (EU) efforts to establish food originality, there is a global need to create Protected Geographical Indication (PGI) and Protected Designation of Origin (PDO) of crops and agricultural products to ensure the integrity of the food supply. In the present work, Visible and Near-Infrared Spectroscopy (Vis-NIR) combined with machine learning techniques was used to discriminate 16 millet varieties (n = 480) originating from various regions of China. Five different machine learning algorithms, namely, K-nearest neighbor (K-NN), Linear discriminant analysis (LDA), Logistic regression (LR), Random Forest (RF), and Support vector machine (SVM), were used to train the NIR spectra of these millet samples and to assess their discrimination performance. Visible cluster trends were obtained from the Principal Component Analysis (PCA) of the spectral data. Cross-validation was used to optimize the performance of the models. Overall, the F-Score values were as follows: SVM with 99.5%, accompanied by RF with 99.5%, LDA with 99.5%, K-NN with 99.1%, and LR with 98.8%. Both the linear and non-linear algorithms yielded positive results, but the non-linear models appear slightly better. The study revealed that applying Vis-NIR spectroscopy assisted by machine learning technique can be an essential tool for tracing the origins of millet, contributing to a safe authentication method in a quick, relatively cheap, and non-destructive way.

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Rapid Authentication of 100% Italian Durum Wheat Pasta by FT-NIR Spectroscopy Combined with Chemometric Tools

Cited by 13 publications

References 35 publications

Discriminate geographical origin of cabe jamu (Piper retrofractum vahl) using nir spectral fingerprints– a short review

Discriminate geographical origin of cabe jamu (Piper retrofractum vahl) using nir spectral fingerprints– a short review

Research Progress of Applying Infrared Spectroscopy Technology for Detection of Toxic and Harmful Substances in Food

Geographic Origin Discrimination of Millet Using Vis-NIR Spectroscopy Combined with Machine Learning Techniques

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