Application of near infrared (NIR) spectroscopy coupled to chemometrics for dried egg-pasta characterization and egg content quantification

Bevilacqua, Marta; Bucci, Remo; Materazzi, Stefano; Marini, Federico

doi:10.1016/j.foodchem.2012.11.018

Cited by 53 publications

(31 citation statements)

References 18 publications

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“…The major advantages of Fourier transform infrared (FT-IR) spectroscopy over other techniques include fast and easy equipment operation along with the possibility to conduct non-destructive analyses on samples at any state (solid, liquid, paste, gas) with convenient and environmentally friendly sample preparation (Bevilacqua et al 2013). Originally, FT-IR has been used for pure sample analysis exclusively; however, recently, it has been successfully used for mixture analysis as well.…”

Section: Introductionmentioning

confidence: 99%

Assessment of the Hazelnuts Roasting Process by Pressure Differential Scanning Calorimetry and MID-FT-IR Spectroscopy

Ciemniewska‐Żytkiewicz

Bryś

Sujka

et al. 2015

Food Anal. Methods

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The effect of the roasting process on the quality of hazelnuts (Corylus avellana L.) lipidic fraction was investigated by pressure differential scanning calorimetry (PDSC) and middle Fourier transform infrared (MID-FT-IR) spectroscopy. The data obtained were referred to and related with fatty acid composition, free fatty acids content, peroxide value, anisidine value and tocopherols concentration measured by standard techniques. These methods were used to decipher between the roasted hazelnuts (shelled and in-shell) treated in different thermal conditions (100°C, 130°C and 160°C) and time (10, 30 and 60 min). The results of free fatty acids (FFA), peroxide value (PV) and anisidine value remained low for all roasting conditions applied and were within quality requirement ranges. The shell left on hazelnuts during the roasting process limited hazelnuts oxidative deterioration, hence it showed protective function. Minor changes occurred in the fatty acid and tocopherol contents and compositions. PDSC, as an appropriate unbiased method for assessing the oxidative stability, revealed a slight elevation of oxidative stability as the temperature and time of the roasting process increased (p<0.05). Discriminant models constructed with the use of principal components (PC) procedure confirmed the ability of MID-FT-IR to monitor the roasting process within a certain range of temperature and time applied.

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Section: Introductionmentioning

confidence: 99%

Assessment of the Hazelnuts Roasting Process by Pressure Differential Scanning Calorimetry and MID-FT-IR Spectroscopy

Ciemniewska‐Żytkiewicz

Bryś

Sujka

et al. 2015

Food Anal. Methods

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show abstract

“…34 In metabolomics and process chemometrics, it is used in conjunction with multilevel data analysis and as a step after an initial analysis of variance. [35][36][37] It is also used in spectroscopy [38][39][40][41] and in sensory science. [42][43][44]…”

Section: Simultaneous Component Analysismentioning

confidence: 99%

Common and distinct components in data fusion

Smilde

Måge

Næs

et al. 2017

Journal of Chemometrics

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In many areas of science multiple sets of data are collected pertaining to the same system. Examples are food products which are characterized by different sets of variables, bio-processes which are on-line sampled with different instruments, or biological systems of which different genomics measurements are obtained. Data fusion is concerned with analyzing such sets of data simultaneously to arrive at a global view of the system under study. One of the upcoming areas of data fusion is exploring whether the data sets have something in common or not. This gives insight into common and distinct variation in each data set, thereby facilitating understanding the relationships between the data sets. Unfortunately, research on methods to distinguish common and distinct components is fragmented, both in terminology as well as in methods: there is no common ground which hampers comparing methods and understanding their relative merits. This paper provides a unifying framework for this subfield of data fusion by using rigorous arguments from linear algebra. The most frequently used methods for distinguishing common and distinct components are explained in this framework and some practical examples are given of these methods in the areas of (medical) biology and food science.

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“…This entailed correlating each of the three different RVA profiles to each of the seven conventional reference methods used to characterise maize hardness. In particular, LW-PLS2 regression was implemented using Euclidean distance to identify the neighbouring samples and a uniform weighting scheme (Bevilacqua et al, 2012).…”

Section: Quantification Of Hardness Properties In Maize Samplesmentioning

confidence: 99%

“…Subsequently, the use of a non-linear regression technique, i.e. locally weighted partial least squares (LW-PLS) regression (Bevilacqua, Bucci, Materazzi, & Marini, 2012;Centner & Massart, 1998) was used to build a regression model to predict maize hardness from the RVA curves. This was done for all seven reference methods individually.…”

Section: Introductionmentioning

confidence: 99%

Application of Rapid Visco Analyser (RVA) viscograms and chemometrics for maize hardness characterisation

et al. 2015

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Application of near infrared (NIR) spectroscopy coupled to chemometrics for dried egg-pasta characterization and egg content quantification

Cited by 53 publications

References 18 publications

Assessment of the Hazelnuts Roasting Process by Pressure Differential Scanning Calorimetry and MID-FT-IR Spectroscopy

Assessment of the Hazelnuts Roasting Process by Pressure Differential Scanning Calorimetry and MID-FT-IR Spectroscopy

Common and distinct components in data fusion

Application of Rapid Visco Analyser (RVA) viscograms and chemometrics for maize hardness characterisation

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