scholarly journals Authentication of Sorrento Walnuts by NIR Spectroscopy Coupled with Different Chemometric Classification Strategies

2020 ◽  
Vol 10 (11) ◽  
pp. 4003 ◽  
Author(s):  
Luigi Amendola ◽  
Patrizia Firmani ◽  
Remo Bucci ◽  
Federico Marini ◽  
Alessandra Biancolillo
Keyword(s):  
Near Infrared ◽  
Unsaturated Fatty Acids ◽  
External Validation ◽  
Value Added ◽  
Nir Spectroscopy ◽  
Selling Price ◽  
Classification Rate ◽  
South Italy ◽  
Organoleptic Characteristics ◽  
Quality Product

Walnuts have been widely investigated because of their chemical composition, which is particularly rich in unsaturated fatty acids, responsible for different benefits in the human body. Some of these fruits, depending on the harvesting area, are considered a high value-added food, thus resulting in a higher selling price. In Italy, walnuts are harvested throughout the national territory, but the fruits produced in the Sorrento area (South Italy) are commercially valuable for their peculiar organoleptic characteristics. The aim of the present study is to develop a non-destructive and shelf-life compatible method, capable of discriminating common walnuts from those harvested in Sorrento (a town in Southern Italy), considered a high quality product. Two-hundred-and-twenty-seven walnuts (105 from Sorrento and 132 grown in other areas) were analyzed by near-infrared spectroscopy (both whole or shelled), and classified by Partial Least Squares-Discriminant Analysis (PLS-DA). Eventually, two multi-block approaches have been exploited in order to combine the spectral information collected on the shell and on the kernel. One of these latter strategies provided the best results (98.3% of correct classification rate in external validation, corresponding to 1 misclassified object over 60). The present study suggests the proposed strategy is a suitable solution for the discrimination of Sorrento walnuts.

scholarly journals Identification and Quantification of Turmeric Adulteration in Egg-Pasta by Near Infrared Spectroscopy and Chemometrics

2020 ◽  
Vol 10 (8) ◽  
pp. 2647 ◽  
Author(s):  
Alessandra Biancolillo ◽  
Angela Santoro ◽  
Patrizia Firmani ◽  
Federico Marini
Keyword(s):  
Analytical Approach ◽  
Near Infrared ◽  
External Validation ◽  
Nir Spectroscopy ◽  
Classification Model ◽  
Correct Classification ◽  
Correct Classification Rate ◽  
Classification Rate ◽  
Yellow Color ◽  
Egg Pasta

“Egg pasta” is a kind of pasta prepared by adding eggs in the dough; the color of this product is often associated to its quality, as it is proportional to the quantity of egg present in the dough. A possible adulteration on this product is represented by the addition of turmeric (not reported in the label) in the dough. The inclusion of this ingredient (which is minimal, given the strong coloring power of this spice) fraudulently accentuates the yellow color of the product, making it more attractive to the consumer. Given this scenario, the aim of the present work is to develop an analytical approach suitable at detecting the presence of turmeric as an adulterant in egg pasta. One hundred samples of traditional and adulterated egg pasta were analyzed by NIR spectroscopy and PLS-DA (Partial Least Squares Discriminant Analysis) in order to discriminate adulterated and compliant pasta. The classification model provided a total correct classification rate of 97.5% in external validation (40 samples). Eventually, the adulterant was quantified by PLS. This strategy provided satisfying results, achieving a RMSEP (Root Mean Square Error in Prediction) of 0.112 (%-w/w) in external validation.

Quantum mechanically calculated NIR spectra of fatty acids

NIR news ◽  
2017 ◽  
Vol 28 (8) ◽  
pp. 11-16 ◽  
Author(s):  
Justyna Grabska
Keyword(s):  
Fatty Acids ◽  
Near Infrared ◽  
Unsaturated Fatty Acids ◽  
Theoretical Study ◽  
Calculated Data ◽  
Nir Spectroscopy ◽  
Theoretical Modelling ◽  
Aliphatic Chain ◽  
Computing Power ◽  
Technical Article

The advances in theory as well as steady development of the computing power have made quantum mechanical simulation of NIR spectra feasible. Recently, we have demonstrated the ability to accurately reproduce in theory the NIR spectra of several complex biomolecules, including fatty acids. In the present technical article, some of these achievements are overviewed. Examples of theoretical modelling of NIR spectra of short- (aliphatic chain up to four carbon atoms) and medium-chain (aliphatic chain counting six carbon atoms) fatty acids are presented and discussed. The calculated data are used directly for explaining the experimental NIR spectra of these systems. Spectral features distinctive to saturated vs. unsaturated fatty acids are essential in various types of samples typically treated by NIR spectroscopy; i.e. food, tissue, biomaterial, etc. Therefore, the theoretical study offers considerable support for basic and applied NIRS. An example of possible practical application of the results of theoretical study for biochemical studies is provided. The topic discussed here has been presented during the 18th International Conference on Near Infrared Spectroscopy (ICNIRS-2017) in Copenhagen, June 2017.

Visible/Near infrared (VIS/NIR) spectroscopy and multivariate data analysis (MVDA) for identification and quantification of olive leaf spot (OLS) disease

2014 ◽  
Vol 2 (1) ◽  
pp. 1-9
Author(s):  
Nawaf Abu-Khalaf ◽  
Mazen Salman
Keyword(s):  
Leaf Spot ◽  
Near Infrared ◽  
Latent Infection ◽  
Linear Method ◽  
Nir Spectroscopy ◽  
Support Vector ◽  
Olive Leaf ◽  
Classification Rate ◽  
Infection Period ◽  
Linear Svm

Early detection of plant disease requires usually elaborating methods techniques and especially when symptoms are not visible. Olive Leaf Spot (OLS) infecting upper surface of olive leaves has a long latent infection period. In this work, VIS/NIR spectroscopy was used to determine the latent infection and severity of the pathogens. Two different classification methods were used, Partial Least Squared-Discrimination Analysis (PLS-DA) (linear method) and Support Vector Machine (SVM) (non-linear). SVM-classification was able to classify severity levels 0, 1, 2, 3, 4, and 5 with classification rates of 94, 90, 73, 79, 83 and 100%, respectively The overall classification rate was about 86%. PLS-DA was able to classify two different severity groups (first group with severity 0, 1, 2, 3, and second group with severity 4, 5), with a classification rate greater than 95%. The results promote further researches, and the possibility of evaluation OLS in-situ using portable VIS/NIR devices.

Visible/Near infrared (VIS/NIR) spectroscopy and multivariate data analysis (MVDA) for identification and quantification of olive leaf spot (OLS) disease

2014 ◽  
Vol 2 (1) ◽  
pp. 1-9
Author(s):  
Nawaf Abu-Khalaf ◽  
Mazen Salman
Keyword(s):  
Leaf Spot ◽  
Near Infrared ◽  
Latent Infection ◽  
Linear Method ◽  
Nir Spectroscopy ◽  
Support Vector ◽  
Olive Leaf ◽  
Classification Rate ◽  
Infection Period ◽  
Linear Svm

Early detection of plant disease requires usually elaborating methods techniques and especially when symptoms are not visible. Olive Leaf Spot (OLS) infecting upper surface of olive leaves has a long latent infection period. In this work, VIS/NIR spectroscopy was used to determine the latent infection and severity of the pathogens. Two different classification methods were used, Partial Least Squared-Discrimination Analysis (PLS-DA) (linear method) and Support Vector Machine (SVM) (non-linear). SVM-classification was able to classify severity levels 0, 1, 2, 3, 4, and 5 with classification rates of 94, 90, 73, 79, 83 and 100%, respectively The overall classification rate was about 86%. PLS-DA was able to classify two different severity groups (first group with severity 0, 1, 2, 3, and second group with severity 4, 5), with a classification rate greater than 95%. The results promote further researches, and the possibility of evaluation OLS in-situ using portable VIS/NIR devices.

Foliar Damage Assessment and Selection of Cold Resistant Genotypes Using near Infrared Spectra of Eucalyptus Globulus Leaves

2009 ◽  
Vol 17 (4) ◽  
pp. 223-231 ◽  
Author(s):  
Rosario del P. Castillo ◽  
David Contreras ◽  
Matthias Otto ◽  
Jaime Baeza ◽  
Juanita Freer
Keyword(s):  
Eucalyptus Globulus ◽  
Near Infrared ◽  
Cold Resistance ◽  
External Validation ◽  
Scatter Correction ◽  
Nir Spectroscopy ◽  
Near Infrared Spectra ◽  
Orthogonal Signal Correction ◽  
Foliar Damage ◽  
Reference Parameters

Near infrared (NIR) spectroscopy was used to predict cold resistance in Eucalyptus globulus genotypes after acclimatation treatments at low temperatures. Branches of the genotypes were maintained during 31 days in three cold chambers and the NIR spectra of milled leaves were obtained. The samples were subsequently exposed to artificial freezing (with some branches exposed to −2°C) and the foliar damage was assessed by visually estimating the necrotic area of each leaf. These values were used as reference parameters to evaluate cold resistance in the genotypes. A partial least squares (PLS) method was performed using the foliar damage and the NIR spectra of leaves. Spectra were treated with multiplicative scatter correction (MSC) and orthogonal signal correction (OSC). An excellent model was achieved which predicted foliar damage in the genotypes with a low standard error of prediction (3.5%), a high regression coefficient in cross-validation and external validation ( r > 0.9) and a high percentage of the variance explained by the spectra (95.4%). Furthermore, a pattern recognition method, using a regularised discriminant analysis (RDA) of the scores matrix obtained in PLS, in a denominated PLS/RDA on scores strategy, was applied directly to the spectra to classify each genotype as tolerant or sensitive; 100% of the genotypes were correctly assigned. These results demonstrate the advantages of using the NIR spectra of leaves as a rapid, nondestructive tool to evaluate cold resistance in genotypes.

Application of near-infrared spectroscopy for the detection of internal insect infestation inPiceaabiesseed lots

2004 ◽  
Vol 34 (1) ◽  
pp. 76-84 ◽  
Author(s):  
Mulualem Tigabu ◽  
Per Christer Odén ◽  
Tong Yun Shen
Keyword(s):  
Near Infrared ◽  
Nir Spectroscopy ◽  
Least Squares Regression ◽  
Absorption Bands ◽  
Classification Rate ◽  
Data Set ◽  
Storage Lipids ◽  
Nir Absorption ◽  
Seed Origin ◽  
Single Seeds

The use of near-infrared (NIR) spectroscopy to discriminate between uninfested seeds of Picea abies (L.) Karst and seeds infested with Plemeliella abietina Seitn (Hymenoptera, Torymidae) larva is sensitive to seed origin and year of collection. Five seed lots collected during different years from Sweden, Finland, and Belarus were used in this study. Initially, seeds were classified as infested or uninfested with X-radiography, and then, NIR spectra from single seeds were collected with a NIR spectrometer from 1100 to 2498 nm with a resolution of 2 nm. Discriminant models were derived by partial least squares regression using raw and orthogonal signal corrected spectra (OSC). The resulting OSC model developed on a pooled data set was more robust than the raw model and resulted in 100% classification accuracy. Once irrelevant spectral variations were removed by using OSC pretreatment, single-lot calibration models resulted in similar classification rates for the new samples irrespective of origin and year of collection. Dis criminant analyses performed with selected NIR absorption bands also gave nearly 100% classification rate for new samples. The origin of spectral differences between infested and uninfested seeds was attributed to storage lipids and proteins that were completely depleted in the former by the feeding larva.

scholarly journals Techno-Economic Assessment for the Production of Algal Fuels and Value-Added Products: Opportunities for High-Protein Microalgae Conversion

2021 ◽  
Author(s):  
Matthew Wiatrowski ◽  
Bruno C Klein ◽  
Ryan W Davis ◽  
Carlos Quiroz-Arita ◽  
Eric C D Tan ◽  
...  
Keyword(s):  
Unsaturated Fatty Acids ◽  
Strong Dependence ◽  
Value Added ◽  
Hydrocarbon Fuels ◽  
High Protein ◽  
Production Costs ◽  
Selling Price ◽  
Economic Potential ◽  
Oxygenated Fuels ◽  
Mixed Alcohols

Abstract BackgroundMicroalgae possess numerous advantages for use as a feedstock in producing renewable fuels and products, with techno-economic analysis (TEA) frequently used to highlight the economic potential and technical challenges of utilizing this biomass in a biorefinery context. However, many historical TEA studies have focused on the conversion of biomass with elevated levels of carbohydrates and lipids and lower levels of protein, incurring substantial burdens on the ability to achieve high cultivation productivity rates relative to nutrient-replete, high-protein biomass. Given a strong dependence of algal biomass production costs on cultivation productivity, further TEA assessment is needed to understand the economic potential for utilizing potentially lower-cost but lower-quality, high-protein microalgae for biorefinery conversion. ResultsIn this work, we conduct rigorous TEA modeling to assess the economic viability of two conceptual technology pathways for processing proteinaceous algae into a suite of fuels and products. One approach, termed mild oxidative treatment and upgrading (MOTU), makes use of a series of thermo-catalytic operations to upgrade solubilized proteins and carbohydrates to hydrocarbon fuels, while another alternative focuses on the biological conversion of those substrates to oxygenated fuels in the form of mixed alcohols (MA). Both pathways rely on the production of polyurethanes from unsaturated fatty acids and valorization of unconverted solids for use as a material for synthesizing bioplastics. The assessment found similar, albeit slightly higher fuel yields and lower costs for the MA pathway, translating to a residual solids selling price of $899/ton for MA versus $1,033/ton for MOTU as would be required to support a $2.50/gallon gasoline equivalent (GGE) fuel selling price. A variation of the MA pathway including subsequent upgrading of the mixed alcohols to hydrocarbon fuels (MAU) reflected a required solids selling price of $975/ton.ConclusionThe slight advantages observed for the MA pathway are partially attributed to a boundary that stops at oxygenated fuels versus fungible drop-in hydrocarbon fuels through a more complex MOTU configuration, with more comparable results obtained for the MAU scenario. In either case, it was shown that an integrated algal biorefinery can be economical through optimal strategies to utilize and valorize all fractions of the biomass.

Unlocking the Secrets of Terminalia Kernels Using Near-Infrared Spectroscopy

2021 ◽  
pp. 000370282199213
Author(s):  
Eshetu Bobasa ◽  
Michael Netzel ◽  
Anh Dao Thi Phan ◽  
Heather Smyth ◽  
Yasmina Sultanbawa ◽  
...  
Keyword(s):  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Near Infrared Reflectance ◽  
Classification Rate ◽  
Potential Health ◽  
Wide Range ◽  
Fruit Samples ◽  
The Fourier Transform ◽  
Kakadu Plum

In recent years, the native food industry in Australia has increased in both value and volume due to the discovery of a wide range of phytochemicals (e.g., vitamin C, polyphenols) that have potential health benefits. Thus, plant organs and tissues of these native plants are used in a wide range of applications. In particular, the kernel of a native plum, the Kakadu plum ( Terminalia ferdinandiana, Combretaceae) is considered to be rich in lipids and other phytochemical compounds. The aim of this study was to evaluate the use of NIR spectroscopy to analyze and characterize kernel samples and tissues of wild harvest fruit samples. The Fourier transform near-infrared reflectance spectra of cracked kernels, seeds cover tissues, and dry powder Kakadu plum kernels were acquired. Both principal component analysis and partial least squares discriminant analysis were used to analyze and interpret the spectral data. A correct classification rate of 93%, 86%, and 80% was achieved for the identification of kernel provenance using all tissues, seed coats, and the whole nuts, respectively. The results of this study reported for the first time the analysis of Kakadu plum kernels and their tissues using NIR spectroscopy.

Lighting the Ivory Track: Are Near-Infrared and Chemometrics Up to the Job? A Proof of Concept

2019 ◽  
Vol 73 (7) ◽  
pp. 816-822 ◽  
Author(s):  
Aoife Power ◽  
Sandy Ingleby ◽  
James Chapman ◽  
Daniel Cozzolino
Keyword(s):  
Near Infrared ◽  
Mammalian Species ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Proof Of Concept ◽  
Classification Rate ◽  
Complementary Method ◽  
Infrared Reflection ◽  
Rapid Tool

A rapid tool to discriminate rhino horn and ivory samples from different mammalian species based on the combination of near-infrared reflection (NIR) spectroscopy and chemometrics was evaluated. In this study, samples from the Australian Museum mammalogy collection were scanned between 950 nm and 1650 nm using a handheld spectrophotometer and analyzed using principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). An overall correct classification rate of 73.5% was obtained for the classification of all samples. This study demonstrates the potential of NIR spectroscopy coupled with chemometrics as a means of a rapid, nondestructive classification technique of horn and ivory samples sourced from a museum. Near-infrared spectroscopy can be used as an alternative or complementary method in the detection of horn and ivory assisting in the combat of illegal trade and aiding the preservation of at-risk species.

Near Infrared Spectroscopic Characterisation of Secondary Structure Content of Proteins

2008 ◽  
Vol 16 (5) ◽  
pp. 437-444 ◽  
Author(s):  
Leon N. Hsu ◽  
Tanya P. Lin ◽  
Samir U. Sane
Keyword(s):  
Solid State ◽  
Secondary Structure ◽  
Near Infrared ◽  
External Validation ◽  
Nir Spectroscopy ◽  
Wave Number ◽  
Average Error ◽  
Second Derivative ◽  
Secondary Structure Content ◽  
Β Sheet

Near infrared (NIR) spectroscopy was investigated as a non-destructive and rapid method to characterise the secondary structures of proteins in solid state. The absorption spectra of 11 reference proteins (0-46% α-helix, 6-73% β-sheet) were obtained using an Antaris NIR Analyser and quantitatively analysed using a chemometric software program to correlate the NIR spectral data to the secondary structure content obtained from X-ray crystallography and Raman spectroscopy. Simple linear regression analyses of the normalised second derivative NIR spectra indicated that 6289 cm−1 ( R2 = 0.99, RMSEC = 2.6) and 4602 cm−1 ( R2 = 0.96, RMSEC = 5.6) were highly sensitive wave number regions for β-sheet structure and the calibration models were highly predictive for three additional proteins which served as external validation standards (average error 2% and 3%, respectively). The second derivative NIR spectra at 4602 cm−1 was also found to be sensitive to α-helical content ( R2 = 0.93, RMSEC = 5.8) and predictive of the validation standards (average error 6%). The results demonstrate the potential of NIR spectroscopy as a rapid and non-invasive tool for quantification of secondary structure content of proteins in solid state.

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