Volatile Compounds Detection by IR Acousto-Optic Detectors

D’Amico, A.; Natale, Corrado Di; Castro, Fabio Lo; Iarossi, Sergio; Catini, Alexandro; Martinelli, Eugenio

doi:10.1007/978-1-4020-9253-4_2

Cited by 8 publications

(5 citation statements)

References 31 publications

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“…Spectra were obtained from the milk of all cows using a MilkoScan FT6000 (Foss A/S, Hillerød, Denmark) in the laboratory of the Breeders Federation of Trento Province (northeastern Italian Alps). Two milk spectra per animal were collected, covering the region from the short-wavelength infrared (SWIR) to the long-wavelength infrared (LWIR), with a total of 1060 spectral points in the region from 5011 to 925 cm −1 [15]. Milk spectra transmittance (T) was transformed to absorbance (A) using the equation A = log(1/T), and the two spectra from animals within each breed were then averaged before data analysis (Figure 1A).…”

Section: Infrared Milk Spectramentioning

confidence: 99%

Comparison of Single-Breed and Multi-Breed Training Populations for Infrared Predictions of Novel Phenotypes in Holstein Cows

Mota

Pegolo

Baba

et al. 2021

Animals

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In general, Fourier-transform infrared (FTIR) predictions are developed using a single-breed population split into a training and a validation set. However, using populations formed of different breeds is an attractive way to design cross-validation scenarios aimed at increasing prediction for difficult-to-measure traits in the dairy industry. This study aimed to evaluate the potential of FTIR prediction using training set combining specialized and dual-purpose dairy breeds to predict different phenotypes divergent in terms of biological meaning, variability, and heritability, such as body condition score (BCS), serum β-hydroxybutyrate (BHB), and kappa casein (k-CN) in the major cattle breed, i.e., Holstein-Friesian. Data were obtained from specialized dairy breeds: Holstein (468 cows) and Brown Swiss (657 cows), and dual-purpose breeds: Simmental (157 cows), Alpine Grey (75 cows), and Rendena (104 cows), giving a total of 1461 cows from 41 multi-breed dairy herds. The FTIR prediction model was developed using a gradient boosting machine (GBM), and predictive ability for the target phenotype in Holstein cows was assessed using different cross-validation (CV) strategies: a within-breed scenario using 10-fold cross-validation, for which the Holstein population was randomly split into 10 folds, one for validation and the remaining nine for training (10-fold_HO); an across-breed scenario (BS_HO) where the Brown Swiss cows were used as the training set and the Holstein cows as the validation set; a specialized multi-breed scenario (BS+HO_10-fold), where the entire Brown Swiss and Holstein populations were combined then split into 10 folds, and a multi-breed scenario (Multi-breed), where the training set comprised specialized (Holstein and Brown Swiss) and dual-purpose (Simmental, Alpine Grey, and Rendena) dairy cows, combined with nine folds of the Holstein cows. Lastly a Multi-breed CV2 scenario was implemented, assuming the same number of records as the reference scenario and using the same proportions as the multi-breed. Within-Holstein, FTIR predictions had a predictive ability of 0.63 for BCS, 0.81 for BHB, and 0.80 for k-CN. Using a specific breed (Brown Swiss) as the training set for prediction in the Holstein population reduced the prediction accuracy by 10% for BCS, 7% for BHB, and 11% for κ-CN. Notably, the combination of Holstein and Brown Swiss cows in the training set increased the predictive ability of the model by 6%, which was 0.66 for BCS, 0.85 for BHB, and 0.87 for k-CN. Using multiple specialized and dual-purpose animals in the training set outperforms the 10-fold_HO (standard) approach, with an increase in predictive ability of 8% for BCS, 7% for BHB, and 10% for k-CN. When the Multi-breed CV2 was implemented, no improvement was observed. Our findings suggest that FTIR prediction of different phenotypes in the Holstein breed can be improved by including different specialized and dual-purpose breeds in the training population. Our study also shows that predictive ability is enhanced when the size of the training population and the phenotypic variability are increased

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Section: Infrared Milk Spectramentioning

confidence: 99%

Comparison of Single-Breed and Multi-Breed Training Populations for Infrared Predictions of Novel Phenotypes in Holstein Cows

Mota

Pegolo

Baba

et al. 2021

Animals

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“…The VIS-NIR spectroscopy works on the visible region (350-800 nm) and near-infrared region (800-2500 nm) (Sun, 2009). In some scheme, the near-infrared region refers to 750-1400 nm and short-wavelength infrared (SWIR) region refers to 1400-3000 nm (D'Amico et al, 2009). The IR spectroscopy can be divided into three sub-regions: near-infrared (NIR), covering 12800-4000 cm -1 ; mid-infrared (MIR), covering 4000-200 cm -1 ; and far-infrared, covering 200-10 cm −1 (Holler et al, 2007;Sun, 2009).…”

Section: Spectroscopymentioning

confidence: 99%

Honey botanical origin classification using hyperspectral imaging and machine learning

Noviyanto

Abdulla

2020

Journal of Food Engineering

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“…Ultrathin 2D lead chalcogenide NSs and NPLs have recently become a substantial line of research since their tunable absorption and photoluminescence properties are suitable to cover the technologically relevant near-infrared (NIR, 1.65–0.89 eV (750–1400 nm)) and short-wave-infrared (SWIR, 0.89–0.41 eV (1400–3000 nm)) regions of the electromagnetic spectrum, e.g., where glass fiber optics exhibit negligible attenuation. − The optical properties of NSs and NPLs are mainly determined by their strongly confined thickness dimension, giving rise to interesting properties and photophysics different from spherical NCs, albeit at the cost of a higher synthetic complexity . While PbSe NPLs are less comprehensively studied, because they have only recently become synthetically accessible, PbS NSs have been thoroughly investigated.…”

Section: Introductionmentioning

confidence: 99%

Toward Bright Colloidal Near-Infrared Emitters: Surface Passivation of 2D PbSe Nanoplatelets by Metal Halides

et al. 2022

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Colloidal 2D PbSe nanoplatelets (NPLs) are promising near- and short wave-infrared emitters for optoelectronic applications at telecommunication wavelengths. However, their photoluminescence quantum yield (PLQY) is limited by the ubiquitous presence of surface-related trap states. Here, we apply a treatment of colloidal PbSe NPLs with different metal halides (MX2, M = Zn, Cd, Pb; X = F, Cl, Br, I) to improve their emission brightness. A surface passivation of the NPLs by PbI2 leads to the best results with a strongly increased PLQY (27% for PbSe NPLs emitting at 0.98 eV (1265 nm) and up to 61% for PbSe NPLs emitting at 1.25 eV (989 nm)). Simultaneously, the full width at half-maximum of the NPL photoluminescence decreased by 10% after the treatment. X-ray photoelectron spectroscopy and complementary surface treatment of PbSe NPLs with organic halides reveal the combined passivating role of both X-type binding halides X– and Z-type binding metal halides MX2 in enhancing the optical properties of the PbSe NPLs. Our results emphasize the potential of 2D PbSe NPLs for efficient emission tailored for the application in fiber optics.

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Volatile Compounds Detection by IR Acousto-Optic Detectors

Cited by 8 publications

References 31 publications

Comparison of Single-Breed and Multi-Breed Training Populations for Infrared Predictions of Novel Phenotypes in Holstein Cows

Comparison of Single-Breed and Multi-Breed Training Populations for Infrared Predictions of Novel Phenotypes in Holstein Cows

Honey botanical origin classification using hyperspectral imaging and machine learning

Toward Bright Colloidal Near-Infrared Emitters: Surface Passivation of 2D PbSe Nanoplatelets by Metal Halides

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