The Experts below are selected from a list of 3654 Experts worldwide ranked by ideXlab platform

Henriette De Valk - One of the best experts on this subject based on the ideXlab platform.

Romdhane Karoui - One of the best experts on this subject based on the ideXlab platform.

  • a comparison and joint use of mid infrared and fluorescence spectroscopic methods for differentiating between manufacturing processes and sampling zones of ripened Soft Cheeses
    European Food Research and Technology, 2008
    Co-Authors: Romdhane Karoui, Josse Baerdemaeker, Éric Dufour
    Abstract:

    Ten traditional M1 (n = 5) and M2 (n = 5) Soft Cheeses produced from raw milk, and five other stabilised M3 (n = 5) Cheeses manufactured from pasteurised milk, were studied using mid infrared (MIR) and front face fluorescence (FFFS) spectroscopies. MIR (3000–900 cm−1), tryptophan (excitation: 290 nm, emission: 305-450 nm), 400-640 emission spectra (excitation: 380 nm) and vitamin A (excitation: 280–350 nm, emission: 410 nm) spectra were recorded at two sampling zones (external (E) and central (C)) of the investigated Cheeses. When the factorial discriminant analysis (FDA) was applied to the MIR spectra, the classification was not satisfactory. With tryptophan fluorescence spectra, correct classification of 94.4 and 69.4% was observed for the calibration and validation spectra, respectively. Better classification was obtained using vitamin A fluorescence spectra, since 91.8 and 80.6% of the calibration and validation spectra, respectively, were correctly classified. When the first five principal components (PCs) of the PCA extracted from each data set were pooled into a single matrix and analysed by FDA, the classification was considerably improved, obtaining a percentage of correct classification of 100 and 91.7% for the calibration and validation samples, respectively. It was concluded that concatenation of the physico-chemical and spectroscopic data sets is an efficient technique for the identification of Soft cheese varieties.

  • A comparison and joint use of VIS-NIR and MIR spectroscopic methods for the determination of some chemical parameters in Soft Cheeses at external and central zones: a preliminary study
    European Food Research and Technology, 2006
    Co-Authors: Romdhane Karoui, Éric Dufour, Abdul Mounem Mouazen, Robert Schoonheydt, Josse Baerdemaeker
    Abstract:

    There is a strong tendency towards exploring rapid and low cost methods for determining chemical parameters and degree of the ripening of Cheeses. The visible-near infrared (VIS-NIR), mid infrared (MIR) and combination of VIS-NIR and MIR spectroscopic methods for measurements of some selected parameters of Soft Cheeses were compared. Fifteen traditional and stabilised retail Soft Cheeses, differing in manufacturing process were studied. Fat, dry matter (DM), pH, total nitrogen (TN) and water soluble nitrogen (WSN) contents were determined by reference methods and scanned with VIS-NIR and MIR spectrophotometers in reflectance mode. Three separate prediction models were developed from the VIS-NIR, MIR and the joint VIS-NIR-MIR spectra using the partial least square (PLS) regression and leave one-out cross-validation technique. Results showed that fat, DM, TN and WSN were the best predicted with the VIS-NIR models providing the lowest values of the root mean square error of prediction (RMSEP) of 1.32, 0.70, 0.11 and 0.10, respectively. The combination of the VIS-NIR and MIR spectral improved slightly the prediction of only the pH. This suggests using the VIS-NIR for the determination of fat, DM, TN and WSN. The pH can also be predicted from the two techniques with approximate quantitative prediction, while a difference between low and high levels of WSN/TN ratio could be determined by the VIS-NIR, MIR or joint use of VIS-NIR-MIR.

  • feasibility study of discriminating the manufacturing process and sampling zone in ripened Soft Cheeses using attenuated total reflectance mir and fiber optic diffuse reflectance vis nir spectroscopy
    Food Research International, 2006
    Co-Authors: Romdhane Karoui, Abdul Mounem Mouazen, Herman Ramon, Robert A Schoonheydt, Josse Baerdemaeker
    Abstract:

    Abstract The use of visible–near infrared (VIS–NIR) and mid infrared (MIR) spectroscopies for rapid characterisation of 15 traditional and stabilised retail Soft Cheeses, manufactured with different cheese making procedures was described. A fiber-type, VIS–NIR spectrophotometer (Zeiss Corona 45 VIS–NIR) in a measurement range of 315–1700 nm and a Fourier transform spectrometer (IFS 66V/S, Bruker, Belgium) in a measurement range between 3000 and 900 cm −1 were used to scan spectra in reflectance mode at the external (E) and central (C) zones of the investigated Cheeses. The principal component analysis (PCA) applied to the normalised spectral data set (VIS–NIR and MIR) did not provide a good discrimination of Cheeses. Therefore, the factorial discriminant analysis (FDA) was applied separately to the first 5 principal components (PCs) of the PCA performed on the VIS–NIR and MIR data sets. Regarding the MIR spectra, the percentage of samples correctly classified into six groups (three for the E and three for the C zones) by the FDA was 64.8% and 33.3% for the calibration and validation samples, respectively. Better classification was obtained from the VIS–NIR spectra since the percentage of samples correctly classified was 85.2% and 63.2% for the calibration and validation samples, respectively. Finally, a concatenation technique was applied on the first 5 PCs of the PCA performed on the VIS–NIR and MIR data sets. This technique allowed a quite satisfactory classification of the investigated Cheeses according to their manufacturing process and their sampling zone. In this case, correct classifications (CC) of 90.7% and 80.6% were obtained for the calibration and the validation samples, respectively.

  • dynamic testing rheology and fluorescence spectroscopy investigations of surface to centre differences in ripened Soft Cheeses
    International Dairy Journal, 2003
    Co-Authors: Romdhane Karoui, Éric Dufour
    Abstract:

    Abstract The viscoelastic properties and the matrix structures of three different retailed Soft Cheeses (M1, M2 and M3), for which the manufacturing process was varied, were studied from the surface to the centre of the cheese using dynamic rheology and front-face fluorescence spectroscopy. The storage modulus ( G ′) and the loss modulus ( G ″) values of the samples increased from the surface to the inner part of the Cheeses, while strain and tan  δ decreased. Protein tryptophan (excitation: 290 nm; emission: 305–400 nm) and vitamin A (emission: 410 nm; excitation: 250–350 nm) spectra were recorded at 20°C in samples cut from the surface to the centre. For each cheese, the data sets containing fluorescence spectra and rheology data were evaluated using multidimensional statistical methods. In addition, the three Cheeses were well discriminated by their spectra by applying factorial discriminant analysis. From the tryptophan fluorescence data sets, 94% and 87.7% good classifications were observed for calibration and validation groups, respectively. A better classification (100% and 96% for principal and test samples) was obtained from the vitamin A spectra. Canonical correlation analysis was performed on the rheology and tryptophan fluorescence spectral data sets, and on the rheology and vitamin A fluorescence spectra data sets. The two groups of variables were found to be highly correlated since the squared canonical coefficients for canonical variates 1, 2, 3, 4 were higher than 0.98. These high correlations indicate that cheese rheology is a reflection of its structure at the molecular level.

Éric Dufour - One of the best experts on this subject based on the ideXlab platform.

  • a comparison and joint use of mid infrared and fluorescence spectroscopic methods for differentiating between manufacturing processes and sampling zones of ripened Soft Cheeses
    European Food Research and Technology, 2008
    Co-Authors: Romdhane Karoui, Josse Baerdemaeker, Éric Dufour
    Abstract:

    Ten traditional M1 (n = 5) and M2 (n = 5) Soft Cheeses produced from raw milk, and five other stabilised M3 (n = 5) Cheeses manufactured from pasteurised milk, were studied using mid infrared (MIR) and front face fluorescence (FFFS) spectroscopies. MIR (3000–900 cm−1), tryptophan (excitation: 290 nm, emission: 305-450 nm), 400-640 emission spectra (excitation: 380 nm) and vitamin A (excitation: 280–350 nm, emission: 410 nm) spectra were recorded at two sampling zones (external (E) and central (C)) of the investigated Cheeses. When the factorial discriminant analysis (FDA) was applied to the MIR spectra, the classification was not satisfactory. With tryptophan fluorescence spectra, correct classification of 94.4 and 69.4% was observed for the calibration and validation spectra, respectively. Better classification was obtained using vitamin A fluorescence spectra, since 91.8 and 80.6% of the calibration and validation spectra, respectively, were correctly classified. When the first five principal components (PCs) of the PCA extracted from each data set were pooled into a single matrix and analysed by FDA, the classification was considerably improved, obtaining a percentage of correct classification of 100 and 91.7% for the calibration and validation samples, respectively. It was concluded that concatenation of the physico-chemical and spectroscopic data sets is an efficient technique for the identification of Soft cheese varieties.

  • A comparison and joint use of VIS-NIR and MIR spectroscopic methods for the determination of some chemical parameters in Soft Cheeses at external and central zones: a preliminary study
    European Food Research and Technology, 2006
    Co-Authors: Romdhane Karoui, Éric Dufour, Abdul Mounem Mouazen, Robert Schoonheydt, Josse Baerdemaeker
    Abstract:

    There is a strong tendency towards exploring rapid and low cost methods for determining chemical parameters and degree of the ripening of Cheeses. The visible-near infrared (VIS-NIR), mid infrared (MIR) and combination of VIS-NIR and MIR spectroscopic methods for measurements of some selected parameters of Soft Cheeses were compared. Fifteen traditional and stabilised retail Soft Cheeses, differing in manufacturing process were studied. Fat, dry matter (DM), pH, total nitrogen (TN) and water soluble nitrogen (WSN) contents were determined by reference methods and scanned with VIS-NIR and MIR spectrophotometers in reflectance mode. Three separate prediction models were developed from the VIS-NIR, MIR and the joint VIS-NIR-MIR spectra using the partial least square (PLS) regression and leave one-out cross-validation technique. Results showed that fat, DM, TN and WSN were the best predicted with the VIS-NIR models providing the lowest values of the root mean square error of prediction (RMSEP) of 1.32, 0.70, 0.11 and 0.10, respectively. The combination of the VIS-NIR and MIR spectral improved slightly the prediction of only the pH. This suggests using the VIS-NIR for the determination of fat, DM, TN and WSN. The pH can also be predicted from the two techniques with approximate quantitative prediction, while a difference between low and high levels of WSN/TN ratio could be determined by the VIS-NIR, MIR or joint use of VIS-NIR-MIR.

  • dynamic testing rheology and fluorescence spectroscopy investigations of surface to centre differences in ripened Soft Cheeses
    International Dairy Journal, 2003
    Co-Authors: Romdhane Karoui, Éric Dufour
    Abstract:

    Abstract The viscoelastic properties and the matrix structures of three different retailed Soft Cheeses (M1, M2 and M3), for which the manufacturing process was varied, were studied from the surface to the centre of the cheese using dynamic rheology and front-face fluorescence spectroscopy. The storage modulus ( G ′) and the loss modulus ( G ″) values of the samples increased from the surface to the inner part of the Cheeses, while strain and tan  δ decreased. Protein tryptophan (excitation: 290 nm; emission: 305–400 nm) and vitamin A (emission: 410 nm; excitation: 250–350 nm) spectra were recorded at 20°C in samples cut from the surface to the centre. For each cheese, the data sets containing fluorescence spectra and rheology data were evaluated using multidimensional statistical methods. In addition, the three Cheeses were well discriminated by their spectra by applying factorial discriminant analysis. From the tryptophan fluorescence data sets, 94% and 87.7% good classifications were observed for calibration and validation groups, respectively. A better classification (100% and 96% for principal and test samples) was obtained from the vitamin A spectra. Canonical correlation analysis was performed on the rheology and tryptophan fluorescence spectral data sets, and on the rheology and vitamin A fluorescence spectra data sets. The two groups of variables were found to be highly correlated since the squared canonical coefficients for canonical variates 1, 2, 3, 4 were higher than 0.98. These high correlations indicate that cheese rheology is a reflection of its structure at the molecular level.

  • Delineation of the structure of Soft Cheeses at the molecular level by fluorescence spectroscopy—relationship with texture
    International Dairy Journal, 2001
    Co-Authors: Éric Dufour, Marie Francoise Devaux, P Fortier, Sophie Herbert
    Abstract:

    Tryptophan fluorescence spectra of eight different Soft Cheeses were recorded directly on cheese samples using front-face fluorescence spectroscopy. Discriminant ability of the data was investigated by discriminant analysis. A correct classification was observed for 95% and 92% of the calibration and validation samples, respectively. It was concluded that tryptophan fluorescence spectra enable the identity of individual Cheeses to be finger-printed. Canonical correlation analysis was applied to Soft-cheese sensory profile data and fluorescence spectral collection in order to measure the link between the two groups of variables measured on the same samples. The two groups of variables were found highly correlated since the squared canonical coefficients for canonical variates 1 and 2 were 0.93 and 0.80, respectively. A subset of four Cheeses was investigated closely in order to establish a molecular basis of the discrimination. It was shown that molecular level information may be derived from the fluorescence spectra.

  • delineation of the structure of Soft Cheeses at the molecular level by fluorescence spectroscopy relationship with texture
    International Dairy Journal, 2001
    Co-Authors: Éric Dufour, Marie Francoise Devaux, P Fortier, Sophie Herbert
    Abstract:

    Tryptophan fluorescence spectra of eight different Soft Cheeses were recorded directly on cheese samples using front-face fluorescence spectroscopy. Discriminant ability of the data was investigated by discriminant analysis. A correct classification was observed for 95% and 92% of the calibration and validation samples, respectively. It was concluded that tryptophan fluorescence spectra enable the identity of individual Cheeses to be finger-printed. Canonical correlation analysis was applied to Soft-cheese sensory profile data and fluorescence spectral collection in order to measure the link between the two groups of variables measured on the same samples. The two groups of variables were found highly correlated since the squared canonical coefficients for canonical variates 1 and 2 were 0.93 and 0.80, respectively. A subset of four Cheeses was investigated closely in order to establish a molecular basis of the discrimination. It was shown that molecular level information may be derived from the fluorescence spectra.

Josse Baerdemaeker - One of the best experts on this subject based on the ideXlab platform.

  • a comparison and joint use of mid infrared and fluorescence spectroscopic methods for differentiating between manufacturing processes and sampling zones of ripened Soft Cheeses
    European Food Research and Technology, 2008
    Co-Authors: Romdhane Karoui, Josse Baerdemaeker, Éric Dufour
    Abstract:

    Ten traditional M1 (n = 5) and M2 (n = 5) Soft Cheeses produced from raw milk, and five other stabilised M3 (n = 5) Cheeses manufactured from pasteurised milk, were studied using mid infrared (MIR) and front face fluorescence (FFFS) spectroscopies. MIR (3000–900 cm−1), tryptophan (excitation: 290 nm, emission: 305-450 nm), 400-640 emission spectra (excitation: 380 nm) and vitamin A (excitation: 280–350 nm, emission: 410 nm) spectra were recorded at two sampling zones (external (E) and central (C)) of the investigated Cheeses. When the factorial discriminant analysis (FDA) was applied to the MIR spectra, the classification was not satisfactory. With tryptophan fluorescence spectra, correct classification of 94.4 and 69.4% was observed for the calibration and validation spectra, respectively. Better classification was obtained using vitamin A fluorescence spectra, since 91.8 and 80.6% of the calibration and validation spectra, respectively, were correctly classified. When the first five principal components (PCs) of the PCA extracted from each data set were pooled into a single matrix and analysed by FDA, the classification was considerably improved, obtaining a percentage of correct classification of 100 and 91.7% for the calibration and validation samples, respectively. It was concluded that concatenation of the physico-chemical and spectroscopic data sets is an efficient technique for the identification of Soft cheese varieties.

  • A comparison and joint use of VIS-NIR and MIR spectroscopic methods for the determination of some chemical parameters in Soft Cheeses at external and central zones: a preliminary study
    European Food Research and Technology, 2006
    Co-Authors: Romdhane Karoui, Éric Dufour, Abdul Mounem Mouazen, Robert Schoonheydt, Josse Baerdemaeker
    Abstract:

    There is a strong tendency towards exploring rapid and low cost methods for determining chemical parameters and degree of the ripening of Cheeses. The visible-near infrared (VIS-NIR), mid infrared (MIR) and combination of VIS-NIR and MIR spectroscopic methods for measurements of some selected parameters of Soft Cheeses were compared. Fifteen traditional and stabilised retail Soft Cheeses, differing in manufacturing process were studied. Fat, dry matter (DM), pH, total nitrogen (TN) and water soluble nitrogen (WSN) contents were determined by reference methods and scanned with VIS-NIR and MIR spectrophotometers in reflectance mode. Three separate prediction models were developed from the VIS-NIR, MIR and the joint VIS-NIR-MIR spectra using the partial least square (PLS) regression and leave one-out cross-validation technique. Results showed that fat, DM, TN and WSN were the best predicted with the VIS-NIR models providing the lowest values of the root mean square error of prediction (RMSEP) of 1.32, 0.70, 0.11 and 0.10, respectively. The combination of the VIS-NIR and MIR spectral improved slightly the prediction of only the pH. This suggests using the VIS-NIR for the determination of fat, DM, TN and WSN. The pH can also be predicted from the two techniques with approximate quantitative prediction, while a difference between low and high levels of WSN/TN ratio could be determined by the VIS-NIR, MIR or joint use of VIS-NIR-MIR.

  • feasibility study of discriminating the manufacturing process and sampling zone in ripened Soft Cheeses using attenuated total reflectance mir and fiber optic diffuse reflectance vis nir spectroscopy
    Food Research International, 2006
    Co-Authors: Romdhane Karoui, Abdul Mounem Mouazen, Herman Ramon, Robert A Schoonheydt, Josse Baerdemaeker
    Abstract:

    Abstract The use of visible–near infrared (VIS–NIR) and mid infrared (MIR) spectroscopies for rapid characterisation of 15 traditional and stabilised retail Soft Cheeses, manufactured with different cheese making procedures was described. A fiber-type, VIS–NIR spectrophotometer (Zeiss Corona 45 VIS–NIR) in a measurement range of 315–1700 nm and a Fourier transform spectrometer (IFS 66V/S, Bruker, Belgium) in a measurement range between 3000 and 900 cm −1 were used to scan spectra in reflectance mode at the external (E) and central (C) zones of the investigated Cheeses. The principal component analysis (PCA) applied to the normalised spectral data set (VIS–NIR and MIR) did not provide a good discrimination of Cheeses. Therefore, the factorial discriminant analysis (FDA) was applied separately to the first 5 principal components (PCs) of the PCA performed on the VIS–NIR and MIR data sets. Regarding the MIR spectra, the percentage of samples correctly classified into six groups (three for the E and three for the C zones) by the FDA was 64.8% and 33.3% for the calibration and validation samples, respectively. Better classification was obtained from the VIS–NIR spectra since the percentage of samples correctly classified was 85.2% and 63.2% for the calibration and validation samples, respectively. Finally, a concatenation technique was applied on the first 5 PCs of the PCA performed on the VIS–NIR and MIR data sets. This technique allowed a quite satisfactory classification of the investigated Cheeses according to their manufacturing process and their sampling zone. In this case, correct classifications (CC) of 90.7% and 80.6% were obtained for the calibration and the validation samples, respectively.

Gabrielle Jones - One of the best experts on this subject based on the ideXlab platform.