Determination of Sex in Ungulate Herbivores via near Infrared Spectroscopy of Hair: Growing Cattle as a Surrogate Model

2021 ◽  
Vol 99 (Supplement_2) ◽  
pp. 23-24
Author(s):  
Doug R Tolleson
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Bos Taurus ◽  
Wound Age ◽  
Estrus Synchronization ◽  
Chi Square ◽  
Linear Discriminant ◽  
Growing Cattle ◽  
Collection Date

Abstract Near infrared spectroscopy (NIRS) has been used in a variety of medical and veterinary science applications. In particular, NIRS calibrations have been developed in livestock for steroid content in cattle hair, and wound age or stage of healing in hot iron cattle brands. These NIRS applications also have potential utility in forensic science. Portable NIRS instruments facilitate measurements on live animals and or animal samples in the field. The objective of this study was to evaluate the efficacy of determining sex in growing cattle via NIRS of hair utilizing a portable spectrometer. In two consecutive years, log 1/R spectra (350–2500 nm) were collected using an ASD Field Spec fitted with a contact probe. Experimental subjects were Bos taurus cross calves (n = 12, yr 1; n = 14, yr 2) born to cows grazing central Arizona rangeland. Calf age was approximately 60, 90 and 210 d at branding, estrus synchronization, and weaning, respectively. As cattle were gathered for these routine working events, a total of 7 M and 19 F calves were scanned 3 times each over the left ribcage. A linear discriminant function was applied to spectral data in order to determine sample membership in M or F groups at each collection date. Chi-square procedures were used to determine differences (P < 0.05) in proportion of correct identifications per group and collection date. Overall, 86% of F and 72% of M were correctly (P < 0.05) identified. Corresponding values were 82% for F and 71% for M at branding, 100% for F and 89% for M at estrus synchronization, and 86% for F and 64% for M at weaning. Calf sex was successfully determined using portable NIRS in this proof of concept study. Efficacy of this method should be evaluated for different ungulate herbivores and under additional collection scenarios.

DISCRIMINATIVE ANALYSIS OF FUNCTIONAL NEAR-INFRARED SPECTROSCOPY SIGNALS FOR DEVELOPMENT OF NEUROIMAGING BIOMARKERS OF ELDERLY DEPRESSION

2010 ◽  
Vol 03 (01) ◽  
pp. 69-74 ◽  
Author(s):  
YE ZHU ◽  
TIANZI JIANG ◽  
YUAN ZHOU ◽  
LISHA ZHAO
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Psychiatric Patients ◽  
Clinical Tool ◽  
Functional Near Infrared Spectroscopy ◽  
Classification Rate ◽  
Linear Discriminant ◽  
Discriminative Model ◽  
Neuroimaging Biomarkers

Functional near-infrared spectroscopy (fNIRS) is a neuroimaging technology which is suitable for psychiatric patients. Several fNIRS studies have found abnormal brain activations during cognitive tasks in elderly depression. In this paper, we proposed a discriminative model of multivariate pattern classification based on fNIRS signals to distinguish elderly depressed patients from healthy controls. This model used the brain activation patterns during a verbal fluency task as features of classification. Then Pseudo-Fisher Linear Discriminant Analysis was performed on the feature space to generate discriminative model. Using leave-one-out (LOO) cross-validation, our results showed a correct classification rate of 88%. The discriminative model showed its ability to identify people with elderly depression and suggested that fNIRS may be an efficient clinical tool for diagnosis of depression. This study may provide the first step for the development of neuroimaging biomarkers based on fNIRS in psychiatric disorders.

scholarly journals Analysis of Different Classification Techniques for Two-Class Functional Near-Infrared Spectroscopy-Based Brain-Computer Interface

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Noman Naseer ◽  
Nauman Khalid Qureshi ◽  
Farzan Majeed Noori ◽  
Keum-Shik Hong
Keyword(s):  
Infrared Spectroscopy ◽  
Discriminant Analysis ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Continuous Wave ◽  
Mental Arithmetic ◽  
Statistical Significance ◽  
Support Vector ◽  
Functional Near Infrared Spectroscopy ◽  
Linear Discriminant

We analyse and compare the classification accuracies of six different classifiers for a two-class mental task (mental arithmetic and rest) using functional near-infrared spectroscopy (fNIRS) signals. The signals of the mental arithmetic and rest tasks from the prefrontal cortex region of the brain for seven healthy subjects were acquired using a multichannel continuous-wave imaging system. After removal of the physiological noises, six features were extracted from the oxygenated hemoglobin (HbO) signals. Two- and three-dimensional combinations of those features were used for classification of mental tasks. In the classification, six different modalities, linear discriminant analysis (LDA), quadratic discriminant analysis (QDA),k-nearest neighbour (kNN), the Naïve Bayes approach, support vector machine (SVM), and artificial neural networks (ANN), were utilized. With these classifiers, the average classification accuracies among the seven subjects for the 2- and 3-dimensional combinations of features were 71.6, 90.0, 69.7, 89.8, 89.5, and 91.4% and 79.6, 95.2, 64.5, 94.8, 95.2, and 96.3%, respectively. ANN showed the maximum classification accuracies: 91.4 and 96.3%. In order to validate the results, a statistical significance test was performed, which confirmed that thepvalues were statistically significant relative to all of the other classifiers (p< 0.005) using HbO signals.

Near-Infrared Spectroscopy for Discrimination of Charcoal from Eucalyptus and Native Cerrado Species—Contribution to a Database for Forestry Supervision

2021 ◽  
Author(s):  
Silvana Nisgoski ◽  
Thaís A P Gonçalves ◽  
Júlia Sonsin-Oliveira ◽  
Adriano W Ballarin ◽  
Graciela I B Muñiz
Keyword(s):  
Infrared Spectroscopy ◽  
Discriminant Analysis ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
New Technologies ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Eucalyptus Species ◽  
Linear Discriminant

Abstract The illegal charcoal trade is an internationally well-known forest crime. In Brazil, government agents try to control it using the document of forest origin (DOF). To confirm a load’s legality, the agents must compare it with the declared content of the DOF. However, to identify charcoal is difficult even for specialists in wood anatomy. Hence, new technologies would facilitate the agents’ work. Near-infrared spectroscopy (NIR) provides a rapid and precise response to differentiate carbonized species. Considering the rich Brazilian flora, NIR studies are still underdeveloped. Our work aimed to differentiate charcoals of seven eucalypts and 10 Cerrado species based on NIR analysis and to add information to a charcoal database. Data were collected with a spectrophotometer in reflectance mode. Partial least square regression with discriminant analysis (PLS-DA) and a linear discriminant analysis (LDA) was applied to confirm the performance and potential of NIR spectra to distinguish native Cerrado species from eucalyptus species. Wavenumbers from 4,000 to 6,000 cm−1 and transversal surface presented the best results. NIR had the potential to distinguish eucalypt charcoals from Cerrado species and in comparison to reference samples. NIR is a potential tool for forestry supervision to guarantee the sustainability of the charcoal supply in Brazil and countries with similar conditions. Study Implications It is a challenge to protect the Cerrado biome against deforestation for charcoal production. The application of new technologies such as near-infrared spectroscopy (NIR) for charcoal identification might improve the work of government agents. In this article, we studied the spectra of Cerrado and eucalypt species. Our results present good separation between the analyzed groups. The main goal is to develop a reliable NIR database that would be useful in the practical work of agents. The database will be available for all control agencies, and future training will be done for a rapid initial evaluation in the field.

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 Algorithm construction methodology for diagnostic classification of near-infrared spectroscopy data

2011 ◽  
Vol 25 (1) ◽  
pp. 1-11 ◽  
Author(s):  
Ramón Guevara ◽  
Lynn Stothers ◽  
Andrew Macnab
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Pelvic Floor Muscle ◽  
Classification And Regression Tree ◽  
Data Sets ◽  
Linear Discriminant ◽  
Cart Analysis ◽  
Chromophore Concentration ◽  
Nirs Data

Background: Near-infrared spectroscopy (NIRS) has recognized potential but limited application for non-invasive diagnostic evaluation. Data analysis methodology that reproducibly distinguishes between the presence or absence of physiologic abnormality could broaden clinical application of this optical technique.Methods: Sample data sets from simultaneous NIRS bladder monitoring and invasive urodynamic pressure-flow studies (UDS) are used to illustrate how a diagnostic algorithm is constructed using classification and regression tree (CART) analysis. Misclassification errors of CART and linear discriminant analysis (LDA) are computed and examples of other urological NIRS data likely amenable to CART analysis presented.Results: CART generated a clinically relevant classification algorithm (error 4%) using 46 data sets of changes in chromophore concentration composed of the whole time series without specifying features. LDA did not (error 16%). Using CART NIRS data provided comparable discriminant ability to the UDS diagnostic nomogram for the presence or absence of obstructive pathology (88% specificity, 84% precision). Pilot data examples from children with and without voiding dysfunction and women with mild or severe pelvic floor muscle dysfunction also show potentially diagnostic differences in chromophore concentration.Conclusions: CART analysis can likely be applied in other NIRS monitoring applications intended to classify patients into those with and without pathology.

scholarly journals A Rapid and Nondestructive Approach for the Classification of Different-Age Citri Reticulatae Pericarpium Using Portable Near Infrared Spectroscopy

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1586
Author(s):  
Pao Li ◽  
Xinxin Zhang ◽  
Shangke Li ◽  
Guorong Du ◽  
Liwen Jiang ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Principal Component ◽  
Nondestructive Method ◽  
Linear Discriminant ◽  
Outer Skin ◽  
Data Pretreatment ◽  
Pca Method

Citri Reticulatae Pericarpium (CRP), has been used in China for hundreds of years as a functional food and medicine. However, some short-age CRPs are disguised as long-age CRPs by unscrupulous businessmen in order to obtain higher profits. In this paper, a rapid and nondestructive method for the classification of different-age CRPs was established using portable near infrared spectroscopy (NIRS) in diffuse reflectance mode combination with appropriate chemometric methods. The spectra of outer skin and inner capsule of CRPs at different storage ages were obtained directly without destroying the samples. Principal component analysis (PCA) with single and combined spectral pretreatment methods was used for the classification of different-age CRPs. Furthermore, the data were pretreated with the PCA method, and Fisher linear discriminant analysis (FLD) with optimized pretreatment methods was discussed for improving the accuracy of classification. Data pretreatment methods can be used to eliminate the noise and background interference. The classification accuracy of inner capsule is better than that of outer skin data. Furthermore, the best results with 100% prediction accuracy can be obtained with FLD method, even without pretreatment.

scholarly journals Detection of primary RGB colors projected on a screen using fNIRS

2017 ◽  
Vol 10 (03) ◽  
pp. 1750006 ◽  
Author(s):  
Xiaolong Liu ◽  
Keum-Shik Hong
Keyword(s):  
Infrared Spectroscopy ◽  
Visual Cortex ◽  
Discriminant Analysis ◽  
Near Infrared Spectroscopy ◽  
Classification Accuracy ◽  
Near Infrared ◽  
Functional Near Infrared Spectroscopy ◽  
Linear Discriminant ◽  
Stimulation Period ◽  
Skewness And Kurtosis

In this study, functional near-infrared spectroscopy (fNIRS) is utilized to measure the hemodynamic responses (HRs) in the visual cortex of 14 subjects (aged 22–34 years) viewing the primary red, green, and blue (RGB) colors displayed on a white screen by a beam projector. The spatiotemporal characteristics of their oxygenated and deoxygenated hemoglobins (HbO and HbR) in the visual cortex are measured using a 15-source and 15-detector optode configuration. To see whether the activation maps upon RGB-color stimuli can be distinguished or not, the [Formula: see text]-values of individual channels are averaged over 14 subjects. To find the best combination of two features for classification, the HRs of activated channels are averaged over nine trials. The HbO mean, peak, slope, skewness and kurtosis values during 2–7[Formula: see text]s window for a given 10[Formula: see text]s stimulation period are analyzed. Finally, the linear discriminant analysis (LDA) for classifying three classes is applied. Individually, the best classification accuracy obtained with slope-skewness features was 74.07% (Subject 1), whereas the best overall over 14 subjects was 55.29% with peak-skewness combination. Noting that the chance level of 3-class classification is 33.33%, it can be said that RGB colors can be distinguished. The overall results reveal that fNIRS can be used for monitoring purposes of the HR patterns in the human visual cortex.

scholarly journals Near infrared spectroscopy as a rapid method for detecting paprika powder adulteration with corn flour

2019 ◽  
pp. 346-352 ◽  
Author(s):  
John-Lewis Zaukuu ◽  
Zsanett Bodor ◽  
Flora Vitalis ◽  
Victoria Zsom-Muha ◽  
Zoltan Kovacs
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Invasive Technique ◽  
Least Squares Regression ◽  
Corn Flour ◽  
Linear Discriminant ◽  
Non Invasive ◽  
Different Levels ◽  
Powdered Form

Paprika powder is a spice of culinary importance in many homes but it?s powdered form, has been targeted for fraudulent activities intended at consumer deception. Diverse methods have been used to investigate some of these adulterations but there is no report of paprika adulteration with corn flour, although it remains a suspicion. Technologies such as the near infrared spectroscopy (NIRS) possess non-invasive and rapid advantages that could be explored to monitor this type of adulteration. The study aimed to discriminate and quantify different levels of paprika powder adulterated with corn flour, using NIRS. Two authentic paprika types (DP and SP) were purchased from reputable sources in Hungary and artificially adulterated in the laboratory. Three repeats of each adulteration level (40%, 30%, 25%, 20%, 15%, 10%, 5%, 3%, 1%) were prepared and scanned with the Metri NIRS respectively, then, analysed with chemometrics: Linear discriminant analysis (LDA) and partial least squares regression (PLSR). LDA showed 100% recognition and prediction accuracies respectively when DP and SP were analyzed separately to discriminate different concentrations of paprika adulteration. LDA models with NIRS recognize corn flour adulteration with 95.55% and predict it with 95.02% accuracy irrespective of the paprika type used in this experiment. PLSR prediction of 40%, 30%, 25%, 20%, 15%, 10%, 5%, 3%, 1% corn flour adulteration yielded an R2CV of 0.98 (high accuracy) and a low RMSECV of 1.71 g/100g (low error). Near infrared as a non-invasive technique exhibited good potentials for paprika powder authentication when corn flour is used as an adulterant.

Oil and Fat Classification by Selected Bands of Near-Infrared Spectroscopy

2000 ◽  
Vol 54 (8) ◽  
pp. 1168-1174 ◽  
Author(s):  
Pierre Hourant ◽  
Vincent Baeten ◽  
Maria T. Morales ◽  
Marc Meurens ◽  
Ramon Aparicio
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
High Performance ◽  
Near Infrared ◽  
Discriminant Functions ◽  
Linear Discriminant ◽  
High Oleic ◽  
Infrared Spectral ◽  
Filter Type ◽  
Different Sources

One hundred and four edible oil and fat samples from 18 different sources, either vegetable (Brazil nut, coconut, corn, sunflower, walnut, virgin olive, peanut, palm, canola, soybean, sunflower) or animal (tallow and hydrogenated fish), have been analyzed by high-performance gas chromatography (HPGC) and near-infrared spectroscopy (NIRS). Fatty acids were quantified by HPGC. The near-infrared spectral features of the most noteworthy bands were studied and discussed to design a filter-type NIR instrument. An arborescent structure, based on stepwise linear discriminant analysis (SLDA), was built to classify the samples according to their sources. Seven discriminant functions permitted a successive discrimination of saturated fats, corn, soybean, sunflower, canola, peanut, high oleic sunflower, and virgin olive oils. The discriminant functions were based on the absorbance values, between three and five, from the 1700–1800 and 2100–2400 nm regions. Chemical explanations are given in support of the selected wavelengths. The arborescent structure was then checked with a test set, and 90% of the samples were correctly classified.

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