Assuring specificity for a multivariate near-infrared (NIR) calibration: The example of the Chambersburg Shoot-out 2002 data set

Norris, Karl H.; Ritchie, Gary E.

doi:10.1016/j.jpba.2008.07.021

Cited by 40 publications

(40 citation statements)

References 4 publications

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“…Such analysis requires a simple yet sturdy model that is capable of maintaining its predictive capability over a prolonged period, coupled with instrumentation that is similarly robust in terms of operational lifetime. The capability of the calibration model to successfully predict unknown samples (i.e., samples not present in the calibration set used to construct the model) must also be assessed; this is done by applying the model to a small number of samples for which the models target property for prediction is defined [3,33,[50][51][52][53][54][55]. Once the model's results are comparable with those of the reference values, the model can be considered to be accurate and useful for determining that target property in the future analysis of unknown samples.…”

Section: Chemometricsmentioning

confidence: 99%

“…An assessment of the model's accuracy is essential to avoid overfitting; consequently, different validation procedures should be applied, as a calibration model without validation is nonsense. In feasibility studies, cross-validation is a practical method to demonstrate that the instrumental method can predict something; however, the predictive ability of the method needs to be demonstrated using an independent validation set [3,33,[50][51][52][53][54][55][56][57][58].…”

Section: Chemometricsmentioning

confidence: 99%

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The Application of State-of-the-Art Analytic Tools (Biosensors and Spectroscopy) in Beverage and Food Fermentation Process Monitoring

et al. 2017

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Abstract:The production of several agricultural products and foods are linked with fermentation. Traditional methods used to control and monitor the quality of the products and processes are based on the use of simple chemical analysis. However, these methods are time-consuming and do not provide sufficient relevant information to guarantee the chemical changes during the process. Commonly used methods applied in the agriculture and food industries to monitor fermentation are those based on simple or single-point sensors, where only one parameter is measured (e.g., temperature or density). These sensors are used several times per day and are often the only source of data available from which the conditions and rate of fermentation are monitored. In the modern food industry, an ideal method to control and monitor the fermentation process should enable a direct, rapid, precise, and accurate determination of several target compounds, with minimal to no sample preparation or reagent consumption. Here, state-of-the-art advancements in both the application of sensors and analytical tools to monitor beverage and food fermentation processes will be discussed.

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Section: Chemometricsmentioning

confidence: 99%

Section: Chemometricsmentioning

confidence: 99%

The Application of State-of-the-Art Analytic Tools (Biosensors and Spectroscopy) in Beverage and Food Fermentation Process Monitoring

et al. 2017

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“…It is generally agreed that the independent set of samples should be sourced from other experiments, batches or conditions (e.g. harvest, different temperatures, moisture content, origin) (Fearn 1997, Cozzolino et al 2009Norris and Ritchie 2008). A common strategy followed by many researches is to split the dataset into two subsets called calibration set (used to construct the model) and validation set (Murray and Cowe 2004;Cozzolino 2014).…”

Section: Model Interpretation and Validationmentioning

confidence: 99%

An Overview on the Application of Chemometrics in Food Science and Technology—An Approach to Quantitative Data Analysis

Roberts

Cozzolino

2016

Food Anal. Methods

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During the last 30 years, food scientists and technologists all over the world are dealing with massive amounts of data derived from the use of different measuring devices (e.g. instrumental and sensory data), the integration of different analytical techniques and processes during the analysis and production of foods. Therefore, complementary disciplines and tools to the traditional ones used in food science such as statistics, experimental design and chemometrics have become essential in modern sciences and are an integral component in the day-to-day analysis of foods and derived products. The aim of this paper is to introduce as well as provide with an overview of different concepts, methods, techniques and general steps used in the quantitative analysis of foods when chemometrics or multivariate analytical methods are applied.

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“…Both approaches, applying EMSC on raw data and applying EMSC on derivated data, are regularly employed. [13][14][15][16][17] It is important to demonstrate the effects and differences of these two approaches, since it is not readily apparent in scientific literature which is the right one to use.…”

Section: Introductionmentioning

confidence: 99%

“…18 In the presented study, the particlar attention was given to the optimization of size of the moving window employed in the SG algorithm during the conversion of recorded spectra into derivative form. Moreover, since the SG and the EMSC preprocessing procedures are often used cooperatively, [14][15][16][17] the optimal sequence of the preprocessing procedures was assessed. The preprocessing methodologies were studied using a real data set and a simulated one where all constituent effects are known.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Savitzky–Golay Parameters for Improving Spectral Resolution and Quantification in Infrared Spectroscopy

2013

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Calculating derivatives of spectral data by the Savitzky-Golay (SG) numerical algorithm is often employed as a preliminary preprocessing step in order to resolve overlapping signals, enhance signal properties, and to suppress unwanted spectral features that arise due to nonideal instrument and sample properties. Addressing these issues, the study on the simulated and the measured infrared data by partial least squares regression has been conducted. The simulated data sets were modelled by considering a range of undesired chemical and physical spectral anomalies and variations that can occur in a measured spectrum, such as baseline variations, noise and scattering effects. The study has demonstrated the importance of optimization of the SG parameters during the conversion of spectra into derivative form, specifically window size and polynomial order of the fitting curve. A specific optimal window size is associated with an exact component in the system being estimated, and this window size does not necessarily apply for some other component present in the system. Since the optimization procedure can be time consuming, as a rough guideline spectral noise level can be used for assessment of window size. Moreover, it has been demonstrated that, when the extended multiplicative signal correction (EMSC) is used alongside the SG procedure, the derivative treatment of data by the SG algorithm must precede the EMSC normalization.

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Assuring specificity for a multivariate near-infrared (NIR) calibration: The example of the Chambersburg Shoot-out 2002 data set

Cited by 40 publications

References 4 publications

The Application of State-of-the-Art Analytic Tools (Biosensors and Spectroscopy) in Beverage and Food Fermentation Process Monitoring

The Application of State-of-the-Art Analytic Tools (Biosensors and Spectroscopy) in Beverage and Food Fermentation Process Monitoring

An Overview on the Application of Chemometrics in Food Science and Technology—An Approach to Quantitative Data Analysis

Optimizing Savitzky–Golay Parameters for Improving Spectral Resolution and Quantification in Infrared Spectroscopy

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