In-line and non-destructive monitoring of core temperature in sausages during industrial heat treatment by NIR interaction spectroscopy

During industrial heat treatment of food products, the core temperature is a critical control parameter with respect to food quality and in particular food safety. This paper presents a novel prototype system based on near infrared spectroscopy (NIRS) that enables continuous in-line and non-contact monitoring of core temperature in sausages during heat treatment in an industrial oven. NIRS interaction measurements in the 761–1081 nm region were used to probe the interior of the sausages. NIRS calibrations for the estimation of core temperature were developed for three different sausage types in the temperature range 60–90 �C. The best accuracy obtained for core temperature with NIRS was about �1.0 �C. Results indicate that calibrations for core temperature can be transferred between different sausage types, which will ease implementation of such a method. The method was successfully tested in a modern sausage production plant.publishedVersio

Unbiased prediction errors for partial least squares regression models: Choosing a representative error estimator for process monitoring

Partial least squares (PLS) regression is widely used to predict chemical analytes from spectroscopic data, thus reducing the need for expensive and time-consuming wet chemical reference analysis in industrial process monitoring. However, predictions via PLS by definition carry sample-specific errors, and estimation of these errors is essential for correct interpretation of results. To increase trust in PLS regression-based predictions, reliable prediction error estimates must be reported. This can be achieved by determining realistic sample-specific prediction errors using an unbiased mean squared prediction error estimate. This work provides a guide for estimating sample-specific prediction errors, showing the importance of choosing an appropriate error estimator prior to deploying PLS models for industrial applications. We reviewed recent and established methods for estimating the sample-specific prediction error and test them through simulation studies. The methods were subsequently applied for estimating prediction errors in two real-life datasets from the food ingredients industry, where near-infrared spectroscopy was used to quantify i) urea in process water and ii) individual protein concentrations in ultrafiltration retentates from a protein fractionation process. Both the simulations and real data examples showed that the mean squared error of calibration is always a downward biased estimator. Although leave-one-out-cross-validation performed surprisingly well in the data analysed in this work, this paper demonstrated that the appropriate choice of error estimator requires the user to make an informed, data-centered decision.publishedVersio

Sample-Specific Prediction Error Measures in Spectroscopy

In applied spectroscopy, the purpose of multivariate calibration is almost exclusively to relate analyte concentrations and spectroscopic measurements. The multivariate calibration model provides estimates of analyte concentrations based on the spectroscopic measurements. Predictive performance is often evaluated based on a mean squared error. While this average measure can be used in model selection, it is not satisfactory for evaluating the uncertainty of individual predictions. For a calibration, the uncertainties are sample specific. This is especially true for multivariate calibration, where interfering compounds may be present. Consider in-line spectroscopic measurements during a chemical reaction, production, etc. Here, reference values are not necessarily available. Hence, one should know the uncertainty of a given prediction in order to use that prediction for telling the state of the chemical reaction, adjusting the process, etc. In this paper, we discuss the influence of variance and bias on sample-specific prediction errors in multivariate calibration. We compare theoretical formulae with results obtained on experimental data. The results point towards the fact that bias contribution cannot necessarily be neglected when assessing sample-specific prediction ability in practice.publishedVersio

Sample-Specific Prediction Error Measures in Spectroscopy

In applied spectroscopy, the purpose of multivariate calibration is almost exclusively to relate analyte concentrations and spectroscopic measurements. The multivariate calibration model provides estimates of analyte concentrations based on the spectroscopic measurements. Predictive performance is often evaluated based on a mean squared error. While this average measure can be used in model selection, it is not satisfactory for evaluating the uncertainty of individual predictions. For a calibration, the uncertainties are sample specific. This is especially true for multivariate calibration, where interfering compounds may be present. Consider in-line spectroscopic measurements during a chemical reaction, production, etc. Here, reference values are not necessarily available. Hence, one should know the uncertainty of a given prediction in order to use that prediction for telling the state of the chemical reaction, adjusting the process, etc. In this paper, we discuss the influence of variance and bias on sample-specific prediction errors in multivariate calibration. We compare theoretical formulae with results obtained on experimental data. The results point towards the fact that bias contribution cannot necessarily be neglected when assessing sample-specific prediction ability in practice

Visualizing indirect correlations when predicting fatty acid composition from near infrared spectroscopy measurements

In recent years, vibrational spectroscopy has been used to predict detailed sample composition like protein and fatty acid profiles. This study shows that fatty acid predictions from near infrared measurements in food stuffs rely on covariance structures amongst the fatty acids. These covariance structures, in turn, vary with factors like breed, age, feed, season etc. and therefore they are not likely to remain constant. Consequently, the robustness and validity of the developed calibration models will be compromised.publishedVersio

In-line and non-destructive monitoring of core temperature in sausages during industrial heat treatment by NIR interaction spectroscopy

publishedVersio

Untargeted classification for paprika powder authentication using visible – Near infrared spectroscopy (VIS-NIRS).

This paper presents a novel strategy for determination of the illegal dye Sudan I in paprika powder. The method is based on fluorescence spectroscopy combined with second-order calibration, which was employed for the first time for this purpose. The method is non-destructive and requires no sample preparation. It was probed that Sudan I exhibited fluorescence; however, the color of paprika samples affected the signal and it was not possible to quantify this adulterant by means of univariate and first-order calibration. To model the effect of variability of color in samples, a central composite experimental design was performed with varying ASTA (American Spices Trade Association) color values and Sudan I concentrations. Different second-order algorithms were tried for quantification. The best results for calibration and validation were obtained from Unfolded-Partial Least-Squares (U-PLS) and Multi-way Partial Least-Squares (N-PLS). The level of detection ranges were 0.4 – 3 mg/g and 0.5 – 3 mg/g for U-PLS and N-PLS, respectively. This was lower than other methods found in the literature.submittedVersio

Estimating dry matter and fat content in Blocks of Swiss cheese during production using on-line near infrared spectroscopy

publishedVersio

Evaluation of multivariate calibration models transferred between spectroscopic instruments:applied to near infrared measurements of flour samples

In a setting where multiple spectroscopic instruments are used for the same measurements it may be convenient to develop the calibration model on a single instrument and then transfer this model to the other instruments. In the ideal scenario, all instruments provide the same predictions for the same samples using the transferred model. However, sometimes the success of a model transfer is evaluated by comparing the transferred model predictions with the reference values. This is not optimal, as uncertainties in the reference method will impact the evaluation. This paper proposes a new method for calibration model transfer evaluation. The new method is based on comparing predictions from different instruments, rather than comparing predictions and reference values. A total of 75 flour samples were available for the study. All samples were measured on ten near infrared (NIR) instruments from two instrumental platforms, five NIR instruments from each platform. Protein content was quantified for all 75 samples and used as the reference variable during modelling by partial least squares regression. By adding artificial noise to first the spectroscopic measurements and then the reference values, this paper highlights the problems of including reference values in the evaluation of a model transfer, as uncertainties in the reference method impact the evaluation. At the same time, this paper highlights the power of the proposed model transfer evaluation, which is based on comparing predictions obtained from the different instruments. In this way, the impact of uncertainties originating from the reference method is minimised