A Note on the Tracing of Herbage Contribution to Grazing Sheep Diet Using Milk and Feces Biomarkers

Milk from grazing ruminants is usually rich in beneficial components for human health, but distinguishing milks sourced from grazing is difficult, and this hinders the valuing of the grazing benefit. This study aimed at evaluating the ability of milk biomarkers (1) to trace milks sourced from sheep submitted to different access times (ATs) to pasture and (2) to estimate sheep herbage dry matter intake (HDMI, g DM ewe−1 d−1) and herbage percentage (HP, % DM) in sheep diet. Animal data derive from a published experiment in which six replicated groups of mid-lactation Sarda sheep had ATs of 2, 4, or 6 h d−1 to a ryegrass pasture. Sheep HDMI and HP of each group were measured on four dates in April 2013. Group milk was sampled, and milk fatty acids (FAs) and n-alkanes were determined by gas chromatography. The latter markers were also measured in feces samples bulked by group. The data (N = 24 records) were submitted to Linear Discriminant Analysis (LDA) aimed at distinguishing the AT to pasture based on biomarkers previously selected by Genetic Algorithms (GA). Partial Least Square Regression (PLSR) models were used to estimate HDMI and HP using biomarkers selected by GA. Based on one milk alkane and six milk FAs as biomarkers, estimates of the AT using GA-LDA were 95.8% accurate. The estimation of HDMI by GA-PLSR based on five milk FAs was moderately precise [explained variance = 75.2%; percentage of the residual mean square error of cross-validation over the mean value (RMSECV%) = 15.0%]. The estimation of HP by GA-PLSR based on 1 milk alkane and 10 FAs was precise (explained variance = 80.8%; RMSECV% = 7.4%). To conclude, these preliminary results suggest that milks sourced from sheep flocks with AT to pasture differentiated by 2 h in the range 2–6 h d−1 can be precisely discriminated using milk biomarkers. The contribution of herbage to sheep diet can also be precisely estimated.

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

Forest Science ◽

10.1093/forsci/fxab015 ◽

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.

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Standard Analytical Methods, Sensory Evaluation, NIRS and Electronic Tongue for Sensing Taste Attributes of Different Melon Varieties

Sensors ◽

10.3390/s19225010 ◽

2019 ◽

Vol 19 (22) ◽

pp. 5010 ◽

Cited By ~ 3

Author(s):

Németh ◽

Balazs ◽

Daood ◽

Kovacs ◽

Bodor ◽

...

Keyword(s):

Electronic Tongue ◽

Storage Conditions ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Sensory Profile ◽

Linear Discriminant ◽

Analytical Instruments ◽

Varietal Classification ◽

And Storage

Grafting by vegetables is a practice with many benefits, but also with some unknown influences on the chemical composition of the fruits. Our goal was to assess the effects of grafting and storage on the extracted juice of four orange-fleshed Cantaloupe type (Celestial, Donatello, Centro, Jannet) melons and two green-fleshed Galia types (Aikido, London), using sensory profile analysis and analytical instruments: An electronic tongue (E-tongue) and near-infrared spectroscopy (NIRS). Both instruments are known for rapid qualitative and quantitative food analysis. Linear discriminant analysis (LDA) was used to classify melons according to their varieties and storage conditions. Partial least square regression (PLSR) was used to predict sensory and standard analytical parameters. Celestial variety had the highest intensity for sensory attributes in Cantaloupe variety. Both green and orange-fleshed melons were discriminated and predicted in LDA with high accuracies (100%) using the E-tongue and NIRS. Galia and Cantaloupe inter-varietal classification with the E-tongue was 89.9% and 82.33%, respectively. NIRS inter-varietal classification was 100% with Celestial variety being the most discriminated as with the sensory results. Both instruments, classified different storage conditions of melons (grafted and self-rooted) with high accuracies. PLSR showed high accuracy for some standard analytical parameters, where significant differences were found comparing different varieties in ANOVA.

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Identification and quantification of additives in bituminous binders based on FTIR spectroscopy and multivariate analysis methods

Materials and Structures ◽

10.1617/s11527-021-01763-1 ◽

2021 ◽

Vol 54 (4) ◽

Author(s):

Sandra Weigel ◽

Michael Gehrke ◽

Christoph Recknagel ◽

Dietmar A. Stephan

Keyword(s):

Ftir Spectroscopy ◽

Climatic Conditions ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Coefficient Of Determination ◽

Linear Discriminant ◽

Bituminous Binders ◽

Identification And Quantification

AbstractBitumen is a crucial building material in road construction, which is exposed to continuously higher stresses due to higher traffic loads and changing climatic conditions. Therefore, various additives are increasingly being added to the bitumen complicating the characterisation of the bituminous binder, especially concerning the reuse of reclaimed asphalt. Therefore, this work aimed to demonstrate that the combination of Fourier transform infrared (FTIR) spectroscopy with attenuated total reflexion (ATR) technique and multivariate evaluation is a very well-suited method to reliable identify and quantify additives in bituminous binders. For this purpose, various unmodified and modified binders, directly and extracted from laboratory and reclaimed asphalts, were investigated with FTIR-ATR spectroscopy. The determined spectra, pre-processed by standard normal variate (SNV) transformation and the determination of the 1st derivation, were evaluated using factor analysis (FA), linear discriminant analysis (LDA) and partial least square regression (PLSR). With this multivariate evaluation, first, a significant model with a very high hit rate of over 90% was developed allowing for the identification of styrene-butadiene copolymers (SBC), ethylene-copolymer bitumen (ECB) and different waxes (e.g. amide and Fischer-Tropsch wax) even if the additives do not show any additional peaks or the samples are multi-modified. Second, a quantification of the content is possible for SBC, ECB, and amide wax with a mean error of RMSE ≤ 0.4 wt% and a coefficient of determination of R2 > 90%. Based on these results, FTIR identification and quantification of additives in bituminous binders is a very promising method with a great potential.

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Prediction of Physicochemical Properties in Honeys with Portable Near-Infrared (microNIR) Spectroscopy Combined with Multivariate Data Processing

Foods ◽

10.3390/foods10020317 ◽

2021 ◽

Vol 10 (2) ◽

pp. 317

Author(s):

Olga Escuredo ◽

María Shantal Rodríguez-Flores ◽

Laura Meno ◽

María Carmen Seijo

Keyword(s):

Physicochemical Properties ◽

Near Infrared ◽

Prediction Models ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Botanical Origin ◽

Linear Discriminant ◽

Physicochemical Composition ◽

Natural Foods

There is an increase in the consumption of natural foods with healthy benefits such as honey. The physicochemical composition contributes to the particularities of honey that differ depending on the botanical origin. Botanical and geographical declaration protects consumers from possible fraud and ensures the quality of the product. The objective of this study was to develop prediction models using a portable near-Infrared (MicroNIR) Spectroscopy to contribute to authenticate honeys from Northwest Spain. Based on reference physicochemical analyses of honey, prediction equations using principal components analysis and partial least square regression were developed. Statistical descriptors were good for moisture, hydroxymethylfurfural (HMF), color (Pfund, L and b* coordinates of CIELab) and flavonoids (RSQ > 0.75; RPD > 2.0), and acceptable for electrical conductivity (EC), pH and phenols (RSQ > 0.61; RDP > 1.5). Linear discriminant analysis correctly classified the 88.1% of honeys based on physicochemical parameters and botanical origin (heather, chestnut, eucalyptus, blackberry, honeydew, multifloral). Estimation of quality and physicochemical properties of honey with NIR-spectra data and chemometrics proves to be a powerful tool to fulfil quality goals of this bee product. Results supported that the portable spectroscopy devices provided an effective tool for the apicultural sector to rapid in-situ classification and authentication of honey.

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التحليل المميز والانحدار اللوجستي بأستعمال المربعات الصغرى الجزئية (دراسة تجريبية (محاكاة))

Journal of Economics and Administrative Sciences ◽

10.33095/jeas.v24i106.32 ◽

2018 ◽

Vol 24 (106) ◽

pp. 407

Author(s):

رباب عبد الرضا صالح ◽

محمد شاكر محمود

Keyword(s):

Logistic Regression ◽

Discriminant Function ◽

Binary Logistic Regression ◽

Partial Least Square ◽

Linear Discriminant Function ◽

Least Square ◽

Partial Least Square Regression ◽

Linear Discriminant ◽

Least Square Regression

يعد اسلوبي الانحدار اللوجستي الثنائي Binary logistic regression والدالة المميزة الخطية Linear discriminant function من اهم الاساليب الاحصائية المستخدمة في التصنيف والتنبؤ، عندما تكون البيانات من النوع الثنائي (0،1) فانه لا يمكن استخدام الانحدار الاعتيادي فلذلك نلجأ الى الانحدار اللوجستي الثنائي والدالة المميزة الخطية في حالة وجود مجموعتين او اكثر، وفي حالة وجود مشكلة التعدد الخطي Multicollinearity بين البيانات (ان البيانات يوجد فيها ارتباطات عالية بين المتغيرات) اصبح عدم الامكان في استخدام الانحدار اللوجستي والدالة المميزة الخطية، ولحل هذه المشكلة توجد عدة طرائق منها طريقة انحدار المربعات الصغرى الجزئية Partial least square regression لحل مشكلة التعدد الخطي. وقد جرى في هذه البحث المقارنة بين الانحدار اللوجستي الثنائي binary logistic regression والدالة المميزة الخطية linear discriminant function عن طريق خطأ التصنيف. حيث تم توليد بيانات بمتغير استجابة (Y) نوع ثنائي (0,1) تحتوي على مشكلة التعدد الخطي وبحجوم عينات (50-100-150-250-400) ومتغيرات (5-10-20). حيث تمت معالجة مشكلة التعدد الخطي بأستعمال طريقة المربعات الصغرى الجزئية Partial least square. وتوصل البحث الى ان الدالة المميزة الخطية linear discriminant function هي أفضل في تصنيف البيانات من الانحدار اللوجستي الثنائي binary logistic regression، اذ صنفت الدالة المميزة البيانات بشكل صحيح وأكثر دقة من الانحدار اللوجستي الثنائي.

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Rapid and Nondestructive Determination of Egg Freshness Category and Marked Date of Lay using Spectral Fingerprint

Journal of Spectroscopy ◽

10.1155/2020/8838542 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11

Author(s):

Thomas O. S. Akowuah ◽

Ernest Teye ◽

Julius Hagan ◽

Kwasi Nyandey

Keyword(s):

Cold Storage ◽

Principal Components ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Linear Discriminant ◽

Identification Rate ◽

Least Square Regression ◽

Ambient Storage ◽

Egg Freshness

The potential of nondestructive prediction of egg freshness based on near-infrared (NIR) spectra fingerprints would be beneficial to quality control officers and consumers alike. In this study, handheld NIR spectrometer in the range of 740 nm to 1070 nm and chemometrics were used to simultaneously determine egg freshness based on marked date of lay for eggs stored under cold and ambient conditions. The spectra acquired from the eggs were preprocessed using multiplicative scatter correction and principal component analysis (MSC-PCA). Linear discriminant analysis (LDA) was used to build identification model to predict the category of freshness, while partial least square regression (PLS-R) was used to determine the marked date of lay. The performance of LDA model was above 95% identification rate in both calibration and prediction set for the eggs stored under ambient and cold storage. For eggs stored in ambient storage, LDA had 95.54% identification rate at 5 principal components, while at cold storage LDA has 100% identification rate at 5 principal components for determining the marked date of lay, and partial least square regression (PLS-R) gave R = 0.87 and RMSEI = 2.57 for ambient storage and R = 0.88 and RMSEI = 2.66 for cold storage in independent set, respectively. The results show that handheld spectrometer and multivariate analysis could be used for rapid and nondestructive measurement of egg freshness. This provides a novel solution for egg integrity prediction along the value chain.

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Field Spectroscopy to Determine Nutritive Value Parameters of Individual Ryegrass Plants

Agronomy ◽

10.3390/agronomy9060293 ◽

2019 ◽

Vol 9 (6) ◽

pp. 293 ◽

Cited By ~ 5

Author(s):

Chaya Smith ◽

Noel Cogan ◽

Pieter Badenhorst ◽

German Spangenberg ◽

Kevin Smith

Keyword(s):

Dry Matter ◽

Nutritive Value ◽

Partial Least Square ◽

Water Soluble ◽

Least Square ◽

Partial Least Square Regression ◽

Soluble Carbohydrates ◽

Breeding Programs ◽

Dry Matter Digestibility

The nutritive value (NV) of perennial ryegrass is an important driver of productivity for grazing stock; therefore, improving NV parameters would be beneficial to meat and dairy producers. NV is not actively targeted by most breeding programs due to NV measurement being prohibitively slow and expensive. Nondestructive spectroscopy has the potential to reduce the time and cost required to screen for NV parameters to make targeted breeding of NV practical. The application of a field spectrometer was trialed to gather canopy spectra of individual ryegrass plants to develop predictive models for eight NV parameters for breeding programs. The targeted NV parameters included acid detergent fibre, ash, crude protein, dry matter, in vivo dry matter digestibility, in vivo organic matter digestibility, neutral detergent fibre, and water-soluble carbohydrates. The models were developed with partial least square regression. Model predicted ranking of plants had R2 between (0.87 and 0.39) and lab rankings of highest preforming plants. The highest ranked plants, which are generally the selection target for breeding programs, were accurately identified with the canopy-based model at a speed, cost and accuracy that is promising for NV breeding programs.

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التحليل المميز والانحدار اللوجستي بوجود مشكلة التعدد الخطي (دراسة تطبيقية على مرض فقر الدم)

Journal of Economics and Administrative Sciences ◽

10.33095/jeas.v23i99.261 ◽

2017 ◽

Vol 23 (99) ◽

pp. 373

Author(s):

رباب عبد الرضا صالح البكري ◽

محمد شاكر محمود

Keyword(s):

Logistic Regression ◽

Discriminant Function ◽

Binary Logistic Regression ◽

Partial Least Square ◽

Linear Discriminant Function ◽

Least Square ◽

Partial Least Square Regression ◽

Linear Discriminant ◽

Least Square Regression

المستخلص تعد طريقة الانحدار اللوجستي الثنائي Binary logistic regression والدالة المميزة الخطية Linear discriminant function من اهم الطرائق الاحصائية المستخدمة في التصنيف والتنبؤ، عندما تكون البيانات من النوع الثنائي (0،1) فانه لا يمكن استخدام الانحدار الاعتيادي فلذلك نلجأ الى الانحدار اللوجستي الثنائي والدالة المميزة الخطية في حالة وجود مجموعتين، وفي حالة وجود مشكلة التعدد الخطي Multicollinearity بين البيانات (ان البيانات يوجد فيها ارتباطات عالية بين المتغيرات) اصبح عدم الامكان في استخدام الانحدار اللوجستي والدالة المميزة الخطية، ولحل هذه المشكلة نلجأ الى طريقة انحدار المربعات الصغرى الجزئية Partial least square regression لحل مشكلة التعدد الخطي. وقد جرى في هذه البحث المقارنة بين الانحدار اللوجستي الثنائي binary logistic regression والدالة المميزة الخطية linear discriminant function عن طريق خطأ التصنيف. حيث تم جمع بيانات عن مرض فقر الدم بمتغيرين هما فقر الدم الحاد بالرمز (0)، وفقر الدم المزمن بالرمز (1) وبعدة متغيرات حول المرض. جمعت البيانات من عدة مستشفيات عراقية، وجمعت عينة من المرضى الراقدين في المستشفى وحالات سابقة رقدت في المستشفى بعينة قدرها (140) مريضاً مصاباً بهذا المرض. وعند اختبار البيانات وجدت ان هناك مشكلة التعدد الخطي Multicollinearity تمت معالجتها بأستعمال طريقة المربعات الصغرى الجزئية Partial least square. وتوصل البحث الى ان الدالة المميزة الخطية linear discriminant function هي أفضل في تصنيف البيانات من الانحدار اللوجستي الثنائي binary logistic regression، اذ صنفت الدالة المميزة البيانات بشكل صحيح وأكثر دقة من الانحدار اللوجستي الثنائي.

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Monitoring the Foliar Nutrients Status of Mango Using Spectroscopy-Based Spectral Indices and PLSR-Combined Machine Learning Models

Remote Sensing ◽

10.3390/rs13040641 ◽

2021 ◽

Vol 13 (4) ◽

pp. 641

Author(s):

Gopal Ramdas Mahajan ◽

Bappa Das ◽

Dayesh Murgaokar ◽

Ittai Herrmann ◽

Katja Berger ◽

...

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Support Vector ◽

Spectral Indices ◽

Learning Models ◽

Leaf Nutrients ◽

Machine Learning Models

Conventional methods of plant nutrient estimation for nutrient management need a huge number of leaf or tissue samples and extensive chemical analysis, which is time-consuming and expensive. Remote sensing is a viable tool to estimate the plant’s nutritional status to determine the appropriate amounts of fertilizer inputs. The aim of the study was to use remote sensing to characterize the foliar nutrient status of mango through the development of spectral indices, multivariate analysis, chemometrics, and machine learning modeling of the spectral data. A spectral database within the 350–1050 nm wavelength range of the leaf samples and leaf nutrients were analyzed for the development of spectral indices and multivariate model development. The normalized difference and ratio spectral indices and multivariate models–partial least square regression (PLSR), principal component regression, and support vector regression (SVR) were ineffective in predicting any of the leaf nutrients. An approach of using PLSR-combined machine learning models was found to be the best to predict most of the nutrients. Based on the independent validation performance and summed ranks, the best performing models were cubist (R2 ≥ 0.91, the ratio of performance to deviation (RPD) ≥ 3.3, and the ratio of performance to interquartile distance (RPIQ) ≥ 3.71) for nitrogen, phosphorus, potassium, and zinc, SVR (R2 ≥ 0.88, RPD ≥ 2.73, RPIQ ≥ 3.31) for calcium, iron, copper, boron, and elastic net (R2 ≥ 0.95, RPD ≥ 4.47, RPIQ ≥ 6.11) for magnesium and sulfur. The results of the study revealed the potential of using hyperspectral remote sensing data for non-destructive estimation of mango leaf macro- and micro-nutrients. The developed approach is suggested to be employed within operational retrieval workflows for precision management of mango orchard nutrients.

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Blood Glucose Level Regression for Smartphone PPG Signals Using Machine Learning

Applied Sciences ◽

10.3390/app11020618 ◽

2021 ◽

Vol 11 (2) ◽

pp. 618

Author(s):

Tanvir Tazul Islam ◽

Md Sajid Ahmed ◽

Md Hassanuzzaman ◽

Syed Athar Bin Amir ◽

Tanzilur Rahman

Keyword(s):

Blood Glucose ◽

Blood Glucose Level ◽

Glucose Level ◽

Quantitative Estimation ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Support Vector ◽

Frequency Noise ◽

Optical Noise

Diabetes is a chronic illness that affects millions of people worldwide and requires regular monitoring of a patient’s blood glucose level. Currently, blood glucose is monitored by a minimally invasive process where a small droplet of blood is extracted and passed to a glucometer—however, this process is uncomfortable for the patient. In this paper, a smartphone video-based noninvasive technique is proposed for the quantitative estimation of glucose levels in the blood. The videos are collected steadily from the tip of the subject’s finger using smartphone cameras and subsequently converted into a Photoplethysmography (PPG) signal. A Gaussian filter is applied on top of the Asymmetric Least Square (ALS) method to remove high-frequency noise, optical noise, and motion interference from the raw PPG signal. These preprocessed signals are then used for extracting signal features such as systolic and diastolic peaks, the time differences between consecutive peaks (DelT), first derivative, and second derivative peaks. Finally, the features are fed into Principal Component Regression (PCR), Partial Least Square Regression (PLS), Support Vector Regression (SVR) and Random Forest Regression (RFR) models for the prediction of glucose level. Out of the four statistical learning techniques used, the PLS model, when applied to an unbiased dataset, has the lowest standard error of prediction (SEP) at 17.02 mg/dL.

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