Application of chemometrics on Raman spectra from Mars: Recent advances and future perspectives

Producción CientíficaThe SuperCam and SHERLOC instruments onboard the NASA/Perseverancerover are returning the first Raman spectra to be ever collected from anotherplanet. Similarly, the RLS instrument onboard the ESA/Rosalind Franklinrover will collect Raman spectra from powdered rocks sampled from thesubsurface of Mars. To optimize the scientific exploitation of Raman spectrareturned from planetary exploration missions, tailored chemometric tools arebeing developed that take into account the analytical capability of the men-tioned Raman spectrometers. In this framework, the ERICA research groupis using laboratory simulators of SuperCam and RLS to perform representa-tive laboratory studies that will enhance the scientific outcome of bothMars2020 and ExoMars missions. On one hand, preliminary studies provedthe chemometric analysis of RLS datasets could be used to obtain a reliablesemi-quantitative estimation of the main mineral phases composing Martiangeological samples. On the other hand, it was proved the data fusion ofRaman and LIBS spectra gathered by SuperCam could be used to enhancethe discrimination of mineral phases from remote geological targets. Besidesdescribing the models developed by the ERICA group, this work presents anoverview of the complementary chemometric approaches so far tested in thisfield of study and propose further improvements to be addressed in thefuture.Ministerio de Economía y Competitividad, Beca/Concesión Número:PID2019-107442RBC31European Union’s Horizon 2020 research and innovation program. grant agreement no. 68730

Authenticity assessment and fraud quantitation of coffee adulterated with chicory, barley and blours by untargeted HPLC-UV-FLD fingerprinting and chemometrics

Coffee, one of the most popular drinks around the world, is also one of the beverages most sus-ceptible of being adulterated. Untargeted high-performance liquid chromatography with ultra-violet and fluorescence detection (HPLC-UV-FLD) fingerprinting strategies in combination with chemometrics were employed for the authenticity assessment and fraud quantitation of adulter-ated coffees involving three different and common adulterants: chicory, barley and flours. The methodologies were applied after a solid-liquid extraction procedure with a methanol:water 50:50 (v/v) solution as extracting solvent. Chromatographic fingerprints were obtained using a Kinetex® C18 reversed-phase column under gradient elution conditions using 0.1% formic acid aqueous solution and methanol as mobile phase components. The obtained coffee and adulter-ants extract HPLC-UV-FLD fingerprints were evaluated by partial least squares regres-sion-discriminants analysis (PLS-DA) resulting to be excellent chemical descriptors for sample discrimination. 100% classification rates for both PLS-DA calibration and prediction models were obtained. Besides, Arabica and Robusta coffee samples were adulterated with chicory, bar-ley and flours, and the obtained HPLC-UV-FLD fingerprints subjected to partial least squares (PLS) regression, demonstrating the feasibility of the proposed methodologies to assess coffee authenticity and to quantify adulteration levels (down to 15%), showing both calibration and prediction errors below 1.3% and 2.4%, respectively

Non-targeted HPLC-FLD fingerprinting for the detection and quantitation of adulterated coffee samples by chemometrics

Coffee is today one of the most popular beverages in the world and the determination of its authenticity is an important issue considering the increase of adulteration cases in the last years. In this work, a simple and efficient non-targeted HPLC-FLD fingerprinting method was employed to detect and quantify adulteration levels in coffee samples by partial least squares (PLS) regression to guarantee food integrity and authenticity. For that purpose, different adulteration cases, involving both coffee production region and variety, were evaluated by pairs (Colombia-Ethiopia, Colombia-Nicaragua, India-Indonesia, Vietnam Arabica-Vietnam Robusta, Vietnam Arabica-Cambodia, and Vietnam Robusta-Cambodia adulteration cases). Overall, the proposed non-targeted HPLC-FLD fingerprinting strategy showed very good results with PLS cross-validation and prediction errors below 3.4% and 7.5%, respectively, for adulteration levels below 15%. Therefore, non-targeted HPLC-FLD fingerprints demonstrated to be suitable to assess coffee integrity and authenticity in the control and prevention of frauds

Assessing the efficiency of Laser-Induced Breakdown Spectroscopy (LIBS) based sorting of post-consumer aluminium scrap

The aluminium Twitch fraction of a Belgian recycling facility could be further sorted by implementing Laser-Induced Breakdown Spectroscopy (LIBS). To achieve this goal, the presented research identifies commercially interesting output fractions and investigates machine learning methods to classify the post-consumer aluminium scrap samples based on the spectral data collected by the LIBS sensor for 834 aluminium scrap pieces. The classification performance is assessed with X-Ray Fluorescence (XRF) reference measurements of the investigated aluminium samples, and expressed in terms of accuracy, precision, recall, and f1 score. Finally, the influence of misclassifications on the composition of the desired output fractions is evaluated.Peer ReviewedPostprint (published version

ANALYSIS OF FOOD COLORANTS USING RAMAN SPECTROSCOPY

Color is one of the most important quality attributes that affect consumers\u27 selection of food. The increasing demand of consumers for natural colorants over artificial ones has placed challenges in both product development and regulatory practices. However, current analytical solutions for food colorants are mostly limited to a sophisticated laboratory setting with tedious sample preparation procedures. Herein, this research focuses on the analytical developments toward cost-effective determination of colorant adulteration and stability analysis. The main technique explored is Raman spectroscopy, which measures the inelastic light scattering and allows one to obtain unique molecular fingerprints for specific molecules. Compared with chromatographic methods, e.g., high-performance liquid chromatography (HPLC) or gas chromatography (GC), Raman spectroscopic methods show great potential in the analysis of food colorants due to their fingerprinting capability non-destructive nature, good portability, and easy preparation protocols. In addition, the integration of gold or silver metallic nano substrate to Raman spectroscopy, known as surface-enhanced Raman spectroscopy (SERS), improves detection sensitivity tremendously. The first study was aimed to develop a fast screening and quantification method combining thin-layer chromatography (TLC) and Raman spectroscopy for saffron powder analysis, which is one of the most expensive agricultural products in the world. Sample solutions of pure saffron and spiked saffron were prepared and dropped on TLC chips for the identification of pure saffron quality as well as discrimination of spiked saffron. The result indicated that Raman spectroscopy was capable of quantifying the major coloring compound, crocin, on dried TLC sample patterns. The method achieved excellent prediction capability for crocin concentration (expressed via absorbance value at 440nm, according to ISO3632) in the range of 0 to 400 with an RMECV of 13 and an R2 of 0.99 using partial least squares (PLS) regression model. The TLC patterns between adulterants (safflower, turmeric, red 40, and yellow 5) were distinct under bright and UV lights. Their variation was demonstrated using principal components analysis (PCA). The lowest adulteration degrees that can be detected were 2.59% for red 40, 4.15% for yellow 5, 31.01% for safflower, and 41.98% for turmeric respectively, using the PLS analysis. The next study was aimed to utilize data fusion strategies to improve the classification and quantification accuracies of saffron adulteration. In the data processing protocol, the imaging data and featured Raman data were concatenated into one data matrix. The model performance of the fused data was compared against each data block. The result indicated that the classification accuracy for saffron adulterants were greatly improved with a classification accuracy of 99% among 4 different adulterants ranging from 2% to 100% (w/w) using the fused data block as compared to the imaging data (84% accuracy) or Raman data (86% accuracy) alone. The quantification performances of the developed PLS model were improved slightly using the fused data block, achieving higher R2 values with lower errors. To improve the detection capability of TLC-Raman for adulterant analysis, a mirror “stamping” TLC-SERS approach was developed. The aim of this study was to build a simple approach for sample separation (TLC) and signal enhancement (SERS) using easily available materials. Homemade silver nanoparticles were fabricated, formed to a mirror, and stamped on developed TLC patterns for Raman signal amplification. Pure saffron (1000 ppm) and spiked saffron (200 ppm red 40) solutions were used as a model system. The result indicated that both saffron colorant(crocin) and adulterated colorant (red 40) exhibited a strong characteristic peak using the mirror stamp approach, whereas there was no or little signal without the mirror application. An arising challenge was reported when food manufacturers were trying to develop products that contain natural colorants with iron fortification. In detail, the natural colorant, anthocyanins, developed unwanted blue color shifts due to iron-anthocyanin co-pigmentation. Thus, the last study was aimed to establish a quantitative model for the prediction of anthocyanin color stability in the presence of dissolved iron. Nine anthocyanin extracts obtained from various sources were purified and measured using SERS. The PCA model successfully discriminated anthocyanins from different plant sources. The stabilization index of each anthocyanin extract was measured using UV-vis spectroscopy under pH 3 and 6 with and without iron fortification (ferric sulfate) and used as input for PLS model. The PLS model demonstrated high accuracy of predicting the stability index with an RMSECV of 2.16 nm (bathochromic shift) and an R2 of 0.98 for external validations. The results from these studies demonstrated the capability of Raman or SERS conjoined with TLC techniques and multivariant analyses for natural colorant adulteration and stability analysis. The Raman/SERS spectral data obtained in the present studies provide useful references for the food colorant research. The established methods could provide useful methodologies for industry applications in regard to fast raw ingredients screening for food companies as well as quality control for natural colorant manufacturers

Application of computational intelligence methods for the automated identification of paper-ink samples based on LIBS

Laser-induced breakdown spectroscopy (LIBS) is an important analysis technique with applications in many industrial branches and fields of scientific research. Nowadays, the advantages of LIBS are impaired by the main drawback in the interpretation of obtained spectra and identification of observed spectral lines. This procedure is highly time-consuming since it is essentially based on the comparison of lines present in the spectrum with the literature database. This paper proposes the use of various computational intelligence methods to develop a reliable and fast classification of quasi-destructively acquired LIBS spectra into a set of predefined classes. We focus on a specific problem of classification of paper-ink samples into 30 separate, predefined classes. For each of 30 classes (10 pens of each of 5 ink types combined with 10 sheets of 5 paper types plus empty pages), 100 LIBS spectra are collected. Four variants of preprocessing, seven classifiers (decision trees, random forest, k-nearest neighbor, support vector machine, probabilistic neural network, multi-layer perceptron, and generalized regression neural network), 5-fold stratified cross-validation, and a test on an independent set (for methods evaluation) scenarios are employed. Our developed system yielded an accuracy of 99.08%, obtained using the random forest classifier. Our results clearly demonstrates that machine learning methods can be used to identify the paper-ink samples based on LIBS reliably at a faster rate

Sistem Pemeringkatan Kualitas Beras Berbasis Teknik Spektroskopi Bimodal: Fourier Transform Infrared (FTIR) dan Laser Induced Breakdown Spectroscopy (LIBS)

Beras merupakan makanan pokok dan menjadi sumber utama karbohidrat bagi masyarakat Indonesia. Indonesia berada pada posisi ketiga dari negara penghasil beras terbesar di dunia. Jenis dan kualitas beras yang dihasilkan pun beragam. Berdasarkan SNI 6128:2015, mutu beras diklasifikasikan berdasarkan bentuk, warna, derajat sosoh dan kadar air dari bulir beras. Disisi lain, beras dikonsumsi untuk memenuhi kebutuhan nutrisi tubuh, sehingga perlu untuk mengetahui kandungan nutrisi dari beras tersebut. Komposisi terbesar dari beras adalah pati, yang mendominasi hingga 85%. Pati terdiri dari 2 karbohidrat polimer yang disebut amilosa dan amilopektin. Kandungan lain yang dimiliki oleh beras adalah zat antioksidan yang dapat mencegah sejumlah penyakit, seperti ; kanker, serangan jantung, penyumbatan pembuluh darah serta melindungi sistem saraf pusat. Molekul yang memiliki peran sebagai antioksidan pada beras adalah fenolik, flavonoid dan antosianin. Guna memenuhi nutrisi dalam tubuh penting untuk mengetahui jenis beras yang memiliki kandungan nutrisi terbaik. Metode yang paling sering digunakan untuk mengetahui kandungan nutrisi pada beras saat ini adalah metode analisis kimia. Penggunaan metode tersebut memerlukan waktu yang cukup lama serta preparasi sampel yang cukup sulit. Oleh sebab itu, metode pengukuran berbasis spektroskopi yang tidak memerlukan persiapan sampel yang rumit adalah solusi untuk menyelesaikan masalah tersebut. Pada penelitian ini, dilakukan sebuah penelitian pendahuluan untuk membangun metoda prediksi yang akurat atas kandungan amilosa, fenolik dan flavonoid, serta elemen yang terkandung pada beras. Metode analisa kimia digunakan untuk mengetahui kadar amilosa, fenolik dan flavonoid pada beras dan sebagai validasi dari sistem prediksi. Sistem prediksi dilakukan menggunakan metode Partial Least Square (PLS) untuk mengetahui kadar amilosa, fenolik, dan flavonoid dari beras berdasarkan spektrum Fourier Transform Infrared (FTIR) yang tervalidasi dengan pengukuran kadar sampel larutan standar. Sehingga, diharapkan nantinya dapat digunakan untuk mengukur kadar amilosa, fenolik dan flavonoid hanya berdasarkan spektrum FTIR dari beras, tanpa perlu dilakukan preparasi sampel yang rumit. Sementara itu, Laser Induced Breakdown Spectroscopy (LIBS) digunakan untuk mengetahui elemen yang terkandung pada beras. Pada penelitian ini juga dilakukan pengelompokan dan klasifikasi kualitas beras menggunakan metode Principal Component Analysis (PCA) berdasarkan spektrum FTIR dan LIBS. Berdasarkan hasil penelitian yang telah dilakukan, didapatkan sistem prediksi kandungan amilosa, fenolik dan flavonoid dengan nilai koefisien determinasi secara berturut-turut adalah 0,95 ; 0,86 ; 0,95 serta nilai RMSE secara berturut-turut adalah 1,4 ; 0,72 ; 0,44. Berdasarkan spektrum LIBS yang didapatkan dari 13 jenis beras, elemen yang terkandung pada beras adalah Mg, Fe, Na, K, Ca, C, H dan O. Hasil pengelompokan berdasarkan spektrum FTIR dan LIBS yang didapatkan, kualitas jenis beras terklasifikasi menjadi 3, yaitu beras kualitas tinggi, beras premium dan beras medium. ================================================================================================================================ Rice is a staple food and the main source of carbohydrates for Indonesian people. Indonesia is in the third position of the world's largest rice producer. The type and quality of rice produced is also varied. Based on SNI 6128:2015, the quality of rice is classified by form, color and moisture content from rice grain. On the other hand, rice is consumed to fulfill the nutritional needs of the body, so it is necessary to know the nutrient content of the rice. The largest composition of rice is starch, which dominates up to 85%. Starch consists of 2 carbohydrate polymers called amylose and amylopectin. Other content belonging to rice is an antioxidant substance that can prevent a number of diseases, such as cancer, heart disease, obstruction of blood vessels and protecting the central nervous system. Molecules that have a role as antioxidants in rice are phenolic, flavonoids and anthocyanins. Therefore, to fulfill nutrients in the body is important to know the type of rice that has the best nutrient content. The most commonly used method to find out nutrient content in rice is a chemical analysis based method. The use of these method requires a considerable time and sample preparation that is quite difficult. Therefore, spectroscopic-based measuring methods that do not require complicated sample preparation are the solution of these problems. In this study, a preliminary research is conducted to develope an accurate prediction method of amylose, phenolic and flavonoid content, also elements contained in rice. Methods of chemical analysis are used to determine the levels of amylose, phenolic and flavonoids in rice which are then used as a validation from prediction system. The predictive system is carried out using the Partial Least Square (PLS) method to determine the levels of amylose, phenolic, and flavonoids from rice based on the Fourier Transform Infrared (FTIR) spectrum that validated with standard solution measurement . Therefore, it is expected that later it can be used to measure the levels of amylose, phenolic and flavonoids only based on the FTIR spectrum of rice, without complicated sample preparation. Meanwhile, Laser Induced Breakdown Spectroscopy (LIBS) is used to figure out the elements contained in the rice. In this research also carried out clustering and classification of rice quality using Principal Component Analysis (PCA) method based on the Spectrum FTIR and LIBS. The results of this research, obtained a prediction system to determine levels of amylose, phenolic and flavonoids with the value of coefficient of determination 0.95; 0.86; 0.95 respectively and the RMSE value 1.4; 0.72; 0.44 respectively. Based on the spectrum of LIBS obtained from 13 types of rice, the elements contained in the rice are Mg, Fe, Na, K, Ca, C, H and O. Results of clustering based on the FTIR spectrum and LIBS obtained, the quality of the type of rice classified into 3, namely High quality rice, premium rice and medium rice

{MALDI}-{TOF} Mass Spectrometry Applications for Food Fraud Detection

Chemical analysis of food products relating to the detection of the most common frauds is a complex task due to the complexity of the matrices and the unknown nature of most processes. Moreover, frauds are becoming more and more sophisticated, making the development of reliable, rapid, cost-effective new analytical methods for food control even more pressing. Over the years, MALDI-TOF MS has demonstrated the potential to meet this need, also due to a series of undeniable intrinsic advantages including ease of use, fast data collection, and capability to obtain valuable information even from complex samples subjected to simple pre-treatment procedures. These features have been conveniently exploited in the field of food frauds in several matrices, including milk and dairy products, oils, fish and seafood, meat, fruit, vegetables, and a few other categories. The present review provides a comprehensive overview of the existing MALDI-based applications for food quality assessment and detection of adulterations

Classification of cowpea beans using multielemental fingerprinting combined with supervised learning

Multielemental compositions (Ag, As, Ba, Be, Cd, Cs, Co, Cr, Cu, Mo, Ni, Pb, Sb, Se, Sn, Sr, Tl, Rb, V, and Zn) of 106 cowpea bean samples belonging to different varieties collected from the province of Corrientes in Argentina were determined using inductively coupled plasma mass spectrometry (ICP-MS). Based on the multielemental data, five supervised learning techniques, namely, linear discriminant analysis (LDA), partial least square discriminant analysis (PLS-DA), k nearest neighbors (k-NN), random forest (RF), and support vector machine (SVM) with radial basis function Kernel, were computed aiming at building classification models that allow one to predict the botanical variety of the samples based on their element profiles. The best classification performance was obtained by SVM with 93% accuracy rate. The model developed through this method enabled the correct separation of the samples into the five cowpea varieties investigated, where 100% sensitivity was achieved for most of the predicted classes. Thus, SVM was the algorithm selected for the classification of the cowpea beans according to their botanical variety. Multielemental determination coupled with supervised pattern recognition techniques have proved to be an interesting approach for differentiating a diverse range of cowpea genotypes. This study has contributed toward generalizing the use of multielemental fingerprinting as a promising tool for testing the authenticity of cowpea beans on a global scale.Fil: Pérez Rodríguez, Michael. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Química Básica y Aplicada del Nordeste Argentino. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Química Básica y Aplicada del Nordeste Argentino; ArgentinaFil: Gaiad, José Emilio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Química Básica y Aplicada del Nordeste Argentino. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Química Básica y Aplicada del Nordeste Argentino; ArgentinaFil: Hidalgo, Melisa Jazmin. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Química Básica y Aplicada del Nordeste Argentino. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Química Básica y Aplicada del Nordeste Argentino; ArgentinaFil: Avanza, María Victoria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Química Básica y Aplicada del Nordeste Argentino. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Química Básica y Aplicada del Nordeste Argentino; ArgentinaFil: Pellerano, Roberto Gerardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Química Básica y Aplicada del Nordeste Argentino. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Química Básica y Aplicada del Nordeste Argentino; Argentin