scholarly journals Origin Identification of Hungarian Honey Using Melissopalynology, Physicochemical Analysis, and Near Infrared Spectroscopy

Molecules ◽  
2021 ◽  
Vol 26 (23) ◽  
pp. 7274
Author(s):  
Zsanett Bodor ◽  
Zoltan Kovacs ◽  
Csilla Benedek ◽  
Géza Hitka ◽  
Hermann Behling
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
External Validation ◽  
Pollen Grains ◽  
Physicochemical Analysis ◽  
Classification Models ◽  
Sweet Chestnut ◽  
Promising Tool ◽  
Origin Identification

The objective of the study was to check the authenticity of Hungarian honey using physicochemical analysis, near infrared spectroscopy, and melissopalynology. In the study, 87 samples from different botanical origins such as acacia, bastard indigo, rape, sunflower, linden, honeydew, milkweed, and sweet chestnut were collected. The samples were analyzed by physicochemical methods (pH, electrical conductivity, and moisture), melissopalynology (300 pollen grains counted), and near infrared spectroscopy (NIRS:740–1700 nm). During the evaluation of the data PCA-LDA models were built for the classification of different botanical and geographical origins, using the methods separately, and in combination (low-level data fusion). PC number optimization and external validation were applied for all the models. Botanical origin classification models were >90% and >55% accurate in the case of the pollen and NIR methods. Improved results were obtained with the combination of the physicochemical, melissopalynology, and NIRS techniques, which provided >99% and >81% accuracy for botanical and geographical origin classification models, respectively. The combination of these methods could be a promising tool for origin identification of honey.

scholarly journals An Improved Residual Network for Pork Freshness Detection Using Near-Infrared Spectroscopy

Entropy ◽  
2021 ◽  
Vol 23 (10) ◽  
pp. 1293
Author(s):  
Liang Zou ◽  
Weinan Liu ◽  
Meng Lei ◽  
Xinhui Yu
Keyword(s):  
Deep Learning ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Evaluation Method ◽  
Rapid Assessment ◽  
Physicochemical Analysis ◽  
Residual Network ◽  
One Dimensional ◽  
Promising Tool

Effective and rapid assessment of pork freshness is significant for monitoring pork quality. However, a traditional sensory evaluation method is subjective and physicochemical analysis is time-consuming. In this study, the near-infrared spectroscopy (NIRS) technique, a fast and non-destructive analysis method, is employed to determine pork freshness. Considering that commonly used statistical modeling methods require preprocessing data for satisfactory performance, this paper presents a one-dimensional squeeze-and-excitation residual network (1D-SE-ResNet) to construct the complex relationship between pork freshness and NIRS. The developed model enhances the one-dimensional residual network (1D-ResNet) with squeeze-and-excitation (SE) blocks. As a deep learning model, the proposed method is capable of extracting features from the input spectra automatically and can be used as an end-to-end model to simplify the modeling process. A comparison between the proposed method and five popular classification models indicates that the 1D-SE-ResNet achieves the best performance, with a classification accuracy of 93.72%. The research demonstrates that the NIRS analysis technique based on deep learning provides a promising tool for pork freshness detection and therefore is helpful for ensuring food safety.

scholarly journals Near-Infrared Spectroscopy (NIRS) and Optical Sensors for Estimating Protein and Fiber in Dryland Mediterranean Pastures

2021 ◽  
Vol 3 (1) ◽  
pp. 73-91
Author(s):  
João Serrano ◽  
Shakib Shahidian ◽  
Ângelo Carapau ◽  
Ana Elisa Rato
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Optical Sensors ◽  
Near Infrared ◽  
Production Systems ◽  
External Validation ◽  
Neutral Detergent Fiber ◽  
Reference Laboratory ◽  
Pasture Quality ◽  
Animal Grazing

Dryland pastures provide the basis for animal sustenance in extensive production systems in Iberian Peninsula. These systems have temporal and spatial variability of pasture quality resulting from the diversity of soil fertility and pasture floristic composition, the interaction with trees, animal grazing, and a Mediterranean climate characterized by accentuated seasonality and interannual irregularity. Grazing management decisions are dependent on assessing pasture availability and quality. Conventional analytical determination of crude protein (CP) and fiber (neutral detergent fiber, NDF) by reference laboratory methods require laborious and expensive procedures and, thus, do not meet the needs of the current animal production systems. The aim of this study was to evaluate two alternative approaches to estimate pasture CP and NDF, namely one based on near-infrared spectroscopy (NIRS) combined with multivariate data analysis and the other based on the Normalized Difference Vegetation Index (NDVI) measured in the field by a proximal active optical sensor (AOS). A total of 232 pasture samples were collected from January to June 2020 in eight fields. Of these, 96 samples were processed in fresh form using NIRS. All 232 samples were dried and subjected to reference laboratory and NIRS analysis. For NIRS, fresh and dry samples were split in two sets: a calibration set with half of the samples and an external validation set with the remaining half of the samples. The results of this study showed significant correlation between NIRS calibration models and reference methods for quantifying pasture quality parameters, with greater accuracy in dry samples (R2 = 0.936 and RPD = 4.01 for CP and R2 = 0.914 and RPD = 3.48 for NDF) than fresh samples (R2 = 0.702 and RPD = 1.88 for CP and R2 = 0.720 and RPD = 2.38 for NDF). The NDVI measured by the AOS shows a similar coefficient of determination to the NIRS approach with pasture fresh samples (R2 = 0.707 for CP and R2 = 0.648 for NDF). The results demonstrate the potential of these technologies for estimating CP and NDF in pastures, which can facilitate the farm manager’s decision making in terms of the dynamic management of animal grazing and supplementation needs.

Prediction of crude protein and neutral detergent fibre concentration in residues of in situ ruminal degradation of pasture samples by near-infrared spectroscopy (NIRS)

2016 ◽  
Vol 56 (9) ◽  
pp. 1504 ◽  
Author(s):  
J. P. Keim ◽  
H. Charles ◽  
D. Alomar
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Crude Protein ◽  
Near Infrared ◽  
External Validation ◽  
Neutral Detergent Fibre ◽  
Insufficient Amount ◽  
Ruminal Degradation ◽  
Animal Feedstuffs

An important constraint of in situ degradability studies is the need to analyse a high number of samples and often with insufficient amount of residue, especially after the longer incubations of high-quality forages, that impede the study of more than one nutritional component. Near-infrared spectroscopy (NIRS) has been established as a reliable method for predicting composition of many entities, including forages and other animal feedstuffs. The objective of this work was to evaluate the potential of NIRS for predicting the crude protein (CP) and neutral detergent fibre (NDF) concentration in rumen incubation residues of permanent and sown temperate pastures in a vegetative stage. In situ residues (n = 236) from four swards were scanned for their visible-NIR spectra and analysed for CP and NDF. Selected equations developed by partial least-squares multivariate regression presented high coefficients of determination (CP = 0.99, NDF = 0.95) and low standard errors (CP = 4.17 g/kg, NDF = 7.91 g/kg) in cross-validation. These errors compare favourably to the average concentrations of CP and NDF (146.5 and 711.2 g/kg, respectively) and represent a low fraction of their standard deviation (CP = 38.2 g/kg, NDF = 34.4 g/kg). An external validation was not as successful, with R2 of 0.83 and 0.82 and a standard error of prediction of 14.8 and 15.2 g/kg, for CP and NDF, respectively. It is concluded that NIRS has the potential to predict CP and NDF of in situ incubation residues of leafy pastures typical of humid temperate zones, but more robust calibrations should be developed.

Rapid Estimation of Xanthan and Biomass Concentration during Xanthan Production under Solid State Fermentation on Polyurethane Foam with Near–Infrared Spectroscopy

2012 ◽  
Vol 482-484 ◽  
pp. 1515-1519
Author(s):  
Zhi Guo Zhang ◽  
Hong Zhang Chen
Keyword(s):  
Infrared Spectroscopy ◽  
Solid State ◽  
Polyurethane Foam ◽  
Near Infrared Spectroscopy ◽  
Solid State Fermentation ◽  
Near Infrared ◽  
External Validation ◽  
Biomass Concentration ◽  
Inert Support ◽  
Xanthan Production

Recently, some solid state fermentation (SSF) processes of xanthan production were studied. However, quantitative analysis of the concentration of xanthan and biomass is more complicated than that of submerged fermentation. To facilitate the analysis of these components, near–infrared spectroscopy (NIRS) was used. A NIRS calibration models for rapidly estimating xanthan and biomass concentration in xanthan fermentation on inert support of polyurethane foam was established. The wavenumber and spectral pretreatment method were optimized. The data of cross validation and external validation shows that NIRS was suitable for rapid and accurate quantification of the concentration of xanthan and biomass in solid state fermentation on inert support. This method will provide much convenience for the research of solid state fermentation on inert support.

scholarly journals Probabilistic classification models for the in situ authentication of iberian pig carcasses using near infrared spectroscopy

Talanta ◽  
2021 ◽  
Vol 222 ◽  
pp. 121511
Author(s):  
Dolores Pérez-Marín ◽  
Tom Fearn ◽  
Cecilia Riccioli ◽  
Emiliano De Pedro ◽  
Ana Garrido
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Classification Models ◽  
Probabilistic Classification ◽  
Iberian Pig ◽  
Pig Carcasses

Rapid identification of epoxy resin and phenolic resin using near infrared spectroscopy

2017 ◽  
Vol 25 (5) ◽  
pp. 324-329 ◽  
Author(s):  
Li Dan ◽  
Wu Yi-Hui
Keyword(s):  
Infrared Spectroscopy ◽  
Epoxy Resin ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Phenolic Resin ◽  
External Validation ◽  
Principal Component ◽  
Rapid Identification ◽  
Classical Analysis ◽  
Prediction Rate

The aim of this research was to investigate the feasibility of Fourier transform near infrared spectroscopy combined with chemometric analysis to develop a rapid method for identification of different resin types which had been deemed similar by a preliminary visual examination. Principal component analysis was applied on spectral data to classify two types of epoxy resin samples and three types of phenolic resin samples. In this case, a total of two hundred and fifteen samples were used for the evaluation and validation of two types of epoxy resin samples (SY1342 and SY1346) and three types of phenolic resin samples (Y3567, Y2705 and Y2137). All were correctly differentiated by their respective models. Moreover, in the external validation, the prediction rate of samples correctly classified was also 100%. Such classifications are very important for the detection of adulterated samples and for quality control. Near infrared spectroscopy was shown to be a very reliable, accurate and useful tool to classify resin samples in a fast, clean and inexpensive way compared to classical analysis, and it will enable copper clad laminate manufacturers to detect and take early corrective actions that will ultimately save time and money while establishing a uniform quality.

scholarly journals Near-Infrared Spectroscopy (NIRS) as a Method for Biological Sex Discrimination in the Endangered Houston Toad (Anaxyrus houstonensis)

2021 ◽  
Vol 5 (1) ◽  
pp. 4
Author(s):  
Li-Dunn Chen ◽  
Mariana Santos-Rivera ◽  
Isabella J. Burger ◽  
Andrew J. Kouba ◽  
Diane M. Barber ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
External Validation ◽  
Sex Discrimination ◽  
Amphibian Species ◽  
Linear Discriminant ◽  
Biological Sex ◽  
Non Invasive ◽  
Houston Toad

Biological sex is one of the more critically important physiological parameters needed for managing threatened animal species because it is crucial for informing several of the management decisions surrounding conservation breeding programs. Near-infrared spectroscopy (NIRS) is a non-invasive technology that has been recently applied in the field of wildlife science to evaluate various aspects of animal physiology and may have potential as an in vivo technique for determining biological sex in live amphibian species. This study investigated whether NIRS could be used as a rapid and non-invasive method for discriminating biological sex in the endangered Houston toad (Anaxyrus houstonensis). NIR spectra (N = 396) were collected from live A. houstonensis individuals (N = 132), and distinct spectral patterns between males and females were identified using chemometrics. Linear discriminant analysis (PCA-LDA) classified the spectra from each biological sex with accuracy ≥ 98% in the calibration and internal validation datasets and 94% in the external validation process. Through the use of NIRS, we have determined that unique spectral signatures can be holistically captured in the skin of male and female anurans, bringing to light the possibility of further application of this technique for juveniles and sexually monomorphic species, whose sex designation is important for breeding-related decisions.

scholarly journals Multivariate method for prediction of fumonisins B1 and B2 and zearalenone in Brazilian maize using Near Infrared Spectroscopy (NIR)

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0244957
Author(s):  
Denize Tyska ◽  
Adriano Olnei Mallmann ◽  
Juliano Kobs Vidal ◽  
Carlos Alberto Araújo de Almeida ◽  
Luciane Tourem Gressler ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
External Validation ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Contamination Level ◽  
Internal Validation ◽  
Significant Difference

Fumonisins (FBs) and zearalenone (ZEN) are mycotoxins which occur naturally in grains and cereals, especially maize, causing negative effects on animals and humans. Along with the need for constant monitoring, there is a growing demand for rapid, non-destructive methods. Among these, Near Infrared Spectroscopy (NIR) has made great headway for being an easy-to-use technology. NIR was applied in the present research to quantify the contamination level of total FBs, i.e., fumonisin B1+fumonisin B2 (FB1+FB2), and ZEN in Brazilian maize. From a total of six hundred and seventy-six samples, 236 were analyzed for FBs and 440 for ZEN. Three regression models were defined: one with 18 principal components (PCs) for FB1, one with 10 PCs for FB2, and one with 7 PCs for ZEN. Partial least square regression algorithm with full cross-validation was applied as internal validation. External validation was performed with 200 unknown samples (100 for FBs and 100 for ZEN). Correlation coefficient (R), determination coefficient (R2), root mean square error of prediction (RMSEP), standard error of prediction (SEP) and residual prediction deviation (RPD) for FBs and ZEN were, respectively: 0.809 and 0.991; 0.899 and 0.984; 659 and 69.4; 682 and 69.8; and 3.33 and 2.71. No significant difference was observed between predicted values using NIR and reference values obtained by Liquid Chromatography Coupled to Tandem Mass Spectrometry (LC-MS/MS), thus indicating the suitability of NIR to rapidly analyze a large numbers of maize samples for FBs and ZEN contamination. The external validation confirmed a fair potential of the model in predicting FB1+FB2 and ZEN concentration. This is the first study providing scientific knowledge on the determination of FBs and ZEN in Brazilian maize samples using NIR, which is confirmed as a reliable alternative methodology for the analysis of such toxins.

scholarly journals Cerebral Oxygenation during Pediatric Cardiac Surgery: Identification of Vulnerable Periods using Near-infrared Spectroscopy

2016 ◽  
Vol 1 (1) ◽  
pp. 15-18
Author(s):  
Mehul Mange ◽  
Manjula Sarkar
Keyword(s):  
Cardiac Surgery ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Cerebral Oxygenation ◽  
Circulatory Arrest ◽  
Supply And Demand ◽  
Pediatric Cardiac Surgery ◽  
Promising Tool ◽  
Neurologic Sequelae

ABSTRACT Objective Neurologic sequelae remain a well-recognized complication of pediatric cardiac surgery. The aetiology of neurologic injury is almost certainly multifactorial, imbalance between cerebral oxygen supply and demand is likely to play an important role. We sought to measure regional cerebral oxygenation in children undergoing cardiac surgery using nearinfrared spectroscopy to ascertain such vulnerable periods. Materials and methods This study is an observational study of 18 children (median age 1.3 years) undergoing cardiac surgery. Regional cerebral oxygenation was monitored using the INVOS3100 cerebral oximeter and related to hemodynamic parameters at each stage of the procedure. Results Prior to the onset of bypass, 10 patients had a decrease in regional cerebral oxygenation, reaching a saturation less than 35% in 5 cases. The most common cause was handling and dissection around the heart prior to and during caval cannulation. With institution of bypass, regional cerebral oxygenation increased. Discontinuation of bypass caused a precipitous decrease in regional cerebral oxygenation in three patients, reaching less than 40%. Conclusions These observations suggest that the pre- and early post-bypass periods are vulnerable times for provision of adequate cerebral oxygenation. Near-infrared spectroscopy is a promising tool for monitoring O2 supply/demand relationships especially during circulatory arrest. How to cite this article Mange M, Sarkar M. Cerebral Oxygenation during Pediatric Cardiac Surgery: Identification of Vulnerable Periods using Near-infrared Spectroscopy. Res Inno Anaesth 2016;1(1):15-18.

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