Discrimination of grassland species and their classification in botanical families by laboratory scale NIR hyperspectral imaging: preliminary results

in Talanta (2013), 116

The objective of this study was to discriminate by a NIR line scan hyperspectral imaging, taxonomic plant families comprised of different grassland species. Plants were collected from semi-natural meadows ... [more ▼]

The objective of this study was to discriminate by a NIR line scan hyperspectral imaging, taxonomic plant families comprised of different grassland species. Plants were collected from semi-natural meadows of the National Apuseni Park, Apuseni Mountains, Gârda area (Romania) according to botanical families. Chemometric tools such as PLS-DA were used to discriminate distinct grassland species, and assign the different species to botanical families. Species within the Poacea family and Other Botanical Families could be distinguished (R2=0.91 and 0.90, respectively) with greater accuracy than those species in the Fabacea family (R2=0.60). A correct classification rate of 99% was obtained in the assignment of the various species to the proper family. Moreover a complete study based on wavelength selection has been performed in order to identify the chemical compound related to each botanical family and therefore to the possible toxicity of the plant. This work could be considered as a first step for the development of a complete procedure for the detection and quantification of possible toxic species in semi-natural meadows used by grazing animals. [less ▲]

Fertilization Effects on the Chemical Composition and In Vitro Organic Matter Digestibility of Semi-natural Meadows as Predicted by NIR Spectrometry

in Notulae Botanicae Horti Agrobotanici Cluj-Napoca (2013), 41(1), 42-48

Management of livestock grazing in highly-productive mountain meadows is an important aspect for the economic viability and the environmental impact of a grassland-based farm. The main aim of this study ... [more ▼]

Management of livestock grazing in highly-productive mountain meadows is an important aspect for the economic viability and the environmental impact of a grassland-based farm. The main aim of this study was to build near infrared models to determine the chemical composition and in vitro organic matter digestibility of Romanian meadow forages. The treatments were organic and mineral fertilizer combinations, and forage samples were obtained from three fertilization experiments conducted in the Apuseni Mountains; these samples were analysed using classical and NIR methods. The samples were scanned in the NIR wavelength band. The CRA-W Gembloux ‘local’ calibration models were validated with Romanian meadow forages and then used in order to predict the forage quality of samples. A second objective of the study was to determine the effects of fertilization on forage quality. The results showed a decrease in crude protein content from the NPK treatment (150:75:75), which can be explained by a reduction of Fabaceae plants with this treatment from 17.25% of the populations in the control (semi-natural meadow not fertilized) to 6.25% in the fertilized plots. The decrease in protein content and in vitro organic matter digestibility was related to a reduced Fabaceae presence. Our recommendation is to use mineral fertilization with NPK doses less than 100:50:50 to improve meadow productivity; meanwhile organic fertilization can also be used to complement and maintain biodiversity and forage quality. [less ▲]

Use of MIR spectrometry for technological properties prediction of milk samples from Walloon dairy cows

Conference (2012, October 12)

Mid-infrared predictions of fatty acids in bovine milk : final results of the RobustMilk project

Poster (2012, August 28)

The development of mid-infrared equations to predict the milk fatty acid (FA) content of milk allows prompt analysis of large numbers of samples and was one of the aims of the RobustMilk project. Data on ... [more ▼]

The development of mid-infrared equations to predict the milk fatty acid (FA) content of milk allows prompt analysis of large numbers of samples and was one of the aims of the RobustMilk project. Data on MIR spectra and FA from multiple countries, production systems, and breeds were used to develop equations to predict milk FA. The calibration set contained 1,776 spectrally different English, Irish, and Belgian milk samples collected for over 6 years. FA were quantified by gas chromatography (GC). Equations were built using partial least squares regression after a first derivative pretreatment applied to the spectral data. The robustness of the developed equations was assessed by cross-validation (CV) using 50 groups from the calibration set. The coefficient of determination (R²) obtained after CV ranged between 0.7101 for the total content of C18:2 and 0.9993 for the saturated FA group. The standard error of CV ranged between 0.0028 and 0.0998 g/dl of milk. Generally, the group or individual FA having the highest content in milk had the highest R²cv. The results obtained in this study confirmed the usefulness of MIR spectra to robustly quantify the FA content of milk permitting the use of these equations by milk laboratories in UK, Belgium or Ireland. Therefore, these equations could be used to develop selection or management tools for dairy farmers in order to improve the nutritional and environmental quality of milk based on the knowledge of the FA composition of their milk. [less ▲]

Mid-infrared predictions of fatty acids in bovine milk : final results of the RobustMilk project

in Book of Abstracts of the 63rd Annual Meeting of the European Federation of Animal Science (2012, August)

The development of mid-infrared equations to predict the milk fatty acid (FA) content of milk allows prompt analysis of large numbers of samples and was one of the aims of the RobustMilk project. Data on ... [more ▼]

The development of mid-infrared equations to predict the milk fatty acid (FA) content of milk allows prompt analysis of large numbers of samples and was one of the aims of the RobustMilk project. Data on MIR spectra and FA from multiple countries, production systems, and breeds were used to develop equations to predict milk FA. The calibration set contained 1,776 spectrally different English, Irish, and Belgian milk samples collected for over 6 years. FA were quantified by gas chromatography (GC). Equations were built using partial least squares regression after a first derivative pretreatment applied to the spectral data. The robustness of the developed equations was assessed by cross-validation (CV) using 50 groups from the calibration set. The coefficient of determination (R²) obtained after CV ranged between 0.7101 for the total content of C18:2 and 0.9993 for the saturated FA group. The standard error of CV ranged between 0.0028 and 0.0998 g/dl of milk. Generally, the group or individual FA having the highest content in milk had the highest R²cv. The results obtained in this study confirmed the usefulness of MIR spectra to robustly quantify the FA content of milk permitting the use of these equations by milk laboratories in UK, Belgium or Ireland. Therefore, these equations could be used to develop selection or management tools for dairy farmers in order to improve the nutritional and environmental quality of milk based on the knowledge of the FA composition of their milk. [less ▲]

Potential use of milk mid-infrared spectra to predict individual methane emission of dairy cows

in Animal (2012), 6(10), 1694-1701

Application de la spectrométrie proche infrarouge à l'analyse de terre

in Actes des 10èmes rencontres de la fertilisation raisonnée et de l'analyse de terre (2011, November)

Mid-infrared predictions of cheese yield from bovine milk

Conference (2011, August 31)

Economically, cheese yield (CY) is very important. Todate, empirical or theoretical formulae allow estimating the theoretical CY from milk fat and casein or protein content of milk. It would be ... [more ▼]

Economically, cheese yield (CY) is very important. Todate, empirical or theoretical formulae allow estimating the theoretical CY from milk fat and casein or protein content of milk. It would be interesting to predict CY during milk recording directly without the need to estimate milk components. Through the BlueSel project, 157 milk samples were collected in Wallonia from individual cows and analyzed using a mid-infrared (MIR) MilkoScanFT6000 spectrometer. Individual laboratory cheese yields (ILCY) were determined for each sample and expressed as g of dry coagulum/100 g of milk dry matter. An equation to predict ILCY from MIR was developed using partial least squared regression (Winisi III). A first derivative pre-treatment of spectra was used to correct the baseline drift. To improve the repeatability of the spectral data, a file which contained the spectra of samples analyzed on 5 spectrometers was used during the calibration. During calibration, 23 outliers were detected a nd removed from the calibration set. The ILCY mean of the final calibration set was 63.9% with a SD of 11.2%. The calibration (C) coefficient of determination (R²) was equal to 0.76 with a standard error (SE) of calibration of 5.5%. A full cross-validation (CV) was preformed to assess the robustness. R²cv was 0.72 with a SECV of 6.0%. The similarity between R²c and R²cv as well as between SEC and SECV permits to consider robustness of the developed equation as good. Even if it is planned to improve the equation with additional samples, this first equation will permit to study ILCY in the Walloon dairy cattle. [less ▲]

Mid-infrared predictions of cheese yield from bovine milk

in Book of Abstracts of the 62nd Annual Meeting of the European Association for Animal Production (2011, August)

Economically, cheese yield (CY) is very important. Todate, empirical or theoretical formulae allow estimating the theoretical CY from milk fat and casein or protein content of milk. It would be ... [more ▼]

Economically, cheese yield (CY) is very important. Todate, empirical or theoretical formulae allow estimating the theoretical CY from milk fat and casein or protein content of milk. It would be interesting to predict CY during milk recording directly without the need to estimate milk components. Through the BlueSel project, 157 milk samples were collected in Wallonia from individual cows and analyzed using a mid-infrared (MIR) MilkoScanFT6000 spectrometer. Individual laboratory cheese yields (ILCY) were determined for each sample and expressed as g of dry coagulum/100 g of milk dry matter. An equation to predict ILCY from MIR was developed using partial least squared regression (Winisi III). A first derivative pre-treatment of spectra was used to correct the baseline drift. To improve the repeatability of the spectral data, a file which contained the spectra of samples analyzed on 5 spectrometers was used during the calibration. During calibration, 23 outliers were detected a nd removed from the calibration set. The ILCY mean of the final calibration set was 63.9% with a SD of 11.2%. The calibration (C) coefficient of determination (R²) was equal to 0.76 with a standard error (SE) of calibration of 5.5%. A full cross-validation (CV) was preformed to assess the robustness. R²cv was 0.72 with a SECV of 6.0%. The similarity between R²c and R²cv as well as between SEC and SECV permits to consider robustness of the developed equation as good. Even if it is planned to improve the equation with additional samples, this first equation will permit to study ILCY in the Walloon dairy cattle. [less ▲]

Mid-Infrared Predictions of Lactoferrin Content in Bovine Milk : Potential Indicator of Mastitis

Conference (2011, July 13)