Advances in NIR spectroscopy applied to process analytical technology in food industries

Polyvinyl alcohol-based capsule shells manufactured by injection molding as ready-to-use moisture barriers for the development of delivery systems

In this work, feasibility of injection molding was demonstrated for manufacturing capsule shells. 600 µm-thick prototypes were successfully molded with pharmaceutical-grade low-viscosity polyvinyl alcohols (PVAs), possibly added with a range of different fillers. They showed reproducible weight and thickness (CV < 2 and 5, respectively), compliant behavior upon piercing (holes diameter analogous to the reference), tunable release performance (immediate and pulsatile), and moisture protection capability. To assess the latter, an on-line method relying on near infrared spectroscopy measurements was set-up and validated. Based on the data collected and considering the versatility IM would provide for product shape/thickness/composition, PVA-based molded shells could help widening the portfolio of ready-to-use capsules, representing an interesting alternative to those commercially available. Indeed, these capsules could be filled with various formulations, even those with stability issues, and intended either for oral administration or for pulmonary delivery via single-dose dry powder inhalers.

NIR sensors combined with chemometric algorithms in intelligent quality evaluation of sweetpotato roots from 'Farm' to 'Table': Progresses, challenges, trends, and prospects

NIR sensors, in conjunction with advanced chemometric algorithms, have proven to be a powerful and efficient tool for intelligent quality evaluation of sweetpotato roots throughout the entire supply chain. By leveraging NIR data in different wavelength ranges, the physicochemical, nutritional and antioxidant compositions, as well as variety classification of sweetpotato roots during the different stages were adequately evaluated, and all findings involving quantitative and qualitative investigations from the beginning to the present were summarized and analyzed comprehensively. All chemometric algorithms including both linear and nonlinear employed in NIR analysis of sweetpotato roots were introduced in detail and their calibration performances in terms of regression and classification were assessed and discussed. The challenges and limitations of current NIR application in quality evaluation of sweetpotato roots are emphasized. The prospects and trends covering the ongoing advancements in software and hardware are suggested to support the sustainable and efficient sweetpotato processing and utilization.

NIR spectroscopy-CNN-enabled chemometrics for multianalyte monitoring in microbial fermentation

As the biopharmaceutical industry looks to implement Industry 4.0, the need for rapid and robust analytical characterization of analytes has become a pressing priority. Spectroscopic tools, like near-infrared (NIR) spectroscopy, are finding increasing use for real-time quantitative analysis. Yet detection of multiple low-concentration analytes in microbial and mammalian cell cultures remains an ongoing challenge, requiring the selection of carefully calibrated, resilient chemometrics for each analyte. The convolutional neural network (CNN) is a puissant tool for processing complex data and making it a potential approach for automatic multivariate spectral processing. This work proposes an inception module-based two-dimensional (2D) CNN approach (I-CNN) for calibrating multiple analytes using NIR spectral data. The I-CNN model, coupled with orthogonal partial least squares (PLS) preprocessing, converts the NIR spectral data into a 2D data matrix, after which the critical features are extracted, leading to model development for multiple analytes. Escherichia coli fermentation broth was taken as a case study, where calibration models were developed for 23 analytes, including 20 amino acids, glucose, lactose, and acetate. The I-CNN model result statistics depicted an average R2 values of prediction 0.90, external validation data set 0.86 and significantly lower root mean square error of prediction values ∼0.52 compared to conventional regression models like PLS. Preprocessing steps were applied to I-CNN models to evaluate any augmentation in prediction performance. Finally, the model reliability was assessed via real-time process monitoring and comparison with offline analytics. The proposed I-CNN method is systematic and novel in extracting distinctive spectral features from a multianalyte bioprocess data set and could be adapted to other complex cell culture systems requiring rapid quantification using spectroscopy.

Future proofing of chondroitin sulphate production: Importance of sustainability and quality for the end-applications

Chondroitin sulphates (CSs) are the most well-known glycosaminoglycans (GAGs) found in any living organism, from microorganisms to invertebrates and vertebrates (including humans), and provide several health benefits. The applications of CSs are numerous including tissue engineering, osteoarthritis treatment, antiviral, cosmetics, and skincare applications. The current commercial production of CSs mostly uses animal, bovine, porcine, and avian tissues as well as marine organisms, marine mammals, sharks, and other fish. The production process consists of tissue hydrolysis, protein removal, and purification using various methods. Mostly, these are chemical-dependent and are complex, multi-step processes. There is a developing trend for abandonment of harsh extraction chemicals and their substitution with different green-extraction technologies, however, these are still in their infancy. The quality of CSs is the first and foremost requirement for end-applications and is dependent on the extraction and purification methodologies used. The final products will show different bio-functional properties, depending on their origin and production methodology. This is a comprehensive review of the characteristics, properties, uses, sources, and extraction methods of CSs. This review emphasises the need for extraction and purification processes to be environmentally friendly and gentle, followed by product analysis and quality control to ensure the expected bioactivity of CSs.

From lab-based to in-line: Analytical tools for the characterization of whey protein denaturation and aggregation-A review

Whey protein denaturation and aggregation have long been areas of research interest to the dairy industry, having significant implications for process performance and final product functionality and quality. As such, a significant number of analytical techniques have been developed or adapted to assess and characterize levels of whey protein denaturation and aggregation, to either maximize processing efficiency or create products with enhanced functionality (both technological and biological). This review aims to collate and critique these approaches based on their analytical principles and outline their application for the assessment of denaturation and aggregation. This review also provides insights into recent developments in process analytical technologies relating to whey protein denaturation and aggregation, whereby some of the analytical methods have been adapted to enable measurements in-line. Developments in this area will enable more live, in-process data to be generated, which will subsequently allow more adaptive processing, enabling improved product quality and processing efficiency. Along with the applicability of these techniques for the assessment of whey protein denaturation and aggregation, limitations are also presented to help assess the suitability of each analytical technique for specific areas of interest.

The role of biospectroscopy and chemometrics as enabling technologies for upcycling of raw materials from the food industry

It is important to utilize the entire animal in meat and fish production to ensure sustainability. Rest raw materials, such as bones, heads, trimmings, and skin, contain essential nutrients that can be transformed into high-value products. Enzymatic protein hydrolysis (EPH) is a bioprocess that can upcycle these materials to create valuable proteins and fats. This paper focuses on the role of spectroscopy and chemometrics in characterizing the quality of the resulting protein product and understanding how raw material quality and processing affect it. The article presents recent developments in chemical characterisation and process modelling, with a focus on rest raw materials from poultry and salmon production. Even if some of the technology is relatively mature and implemented in many laboratories and industries, there are still open challenges and research questions. The main challenges are related to the transition of technology and insights from laboratory to industrial scale, and the link between peptide composition and critical product quality attributes.

Near-Infrared Spectroscopy Procedure for Online Determination of Sodium and Potassium Content in Low-Salt Cured Hams

This paper reports the development of a near-infrared spectroscopy (NIRS) calibration procedure for the determination of sodium and potassium content in cured ham samples. Sliced samples of hams treated with different salts in different percentages were included in the study. Calibration models developed using partial least squares regression were cross-validated and predictive models were tested using the samples of cured ham with low sodium content. The results showed that the developed NIRS procedure is capable of directly measuring the potassium content of packaged dry-cured ham slices with low sodium content with a fitting accuracy of 91.44%, and that it can indirectly determine the sodium content by applying a correction factor to the values obtained for potassium. The prediction error between the calculated and actual sodium values determined using inductively coupled plasma atomic emission spectrophotometry (ICP-AES) was 0.004%, and this confirms that the NIRS procedure is a viable option for the determination of sodium and potassium content in this type of sample.

Real-Time Monitoring of a Sol-Gel Reaction for Polysilane Production Using Inline NIR Spectroscopy

The sol-gel process is an effective method for the preparation of homogeneous structured nanomaterials whose physico-chemical properties strongly depend on the experimental conditions applied. The control of a three-component reaction with silanes showing multiple reaction sites revealed the need for an analytical tool that allows a rapid response to ongoing transformations in the reaction mixture. Herein, we describe the implementation of near-infrared (NIR) spectroscopy based on compact, mechanically robust, and cost-efficient micro-optomechanical system technology in the sol-gel process of three silanes with a total of nine reaction sites. The NIR-spectroscopically controlled reaction yields a long-time stable product with reproducible quality, fulfilling the demanding requirements for further use in coating processes. ¹H nuclear magnetic resonance measurements are used as reference values for the calibration of a partial least squares (PLS) regression model. The precise prediction of the desired parameters from collected NIR spectroscopy data acquired during the sol-gel reaction proves the applicability of the calibrated PLS regression model. The determined shelf-life and further processing tests verify the high quality of the sol-gel and the produced highly cross-linked polysilane.

A Feasibility Study towards the On-Line Quality Assessment of Pesto Sauce Production by NIR and Chemometrics

The food industry needs tools to improve the efficiency of their production processes by minimizing waste, detecting timely potential process issues, as well as reducing the efforts and workforce devoted to laboratory analysis while, at the same time, maintaining high-quality standards of products. This can be achieved by developing on-line monitoring systems and models. The present work presents a feasibility study toward establishing the on-line monitoring of a pesto sauce production process by means of NIR spectroscopy and chemometric tools. The spectra of an intermediate product were acquired on-line and continuously by a NIR probe installed directly on the process line. Principal Component Analysis (PCA) was used both to perform an exploratory data analysis and to build Multivariate Statistical Process Control (MSPC) charts. Moreover, Partial Least Squares (PLS) regression was employed to compute real time prediction models for two different pesto quality parameters, namely, consistency and total lipids content. PCA highlighted some differences related to the origin of basil plants, the main pesto ingredient, such as plant age and supplier. MSPC charts were able to detect production stops/restarts. Finally, it was possible to obtain a rough estimation of the quality of some properties in the early production stage through PLS.

Monitoring Chemical Changes of Coffee Beans During Roasting Using Real-time NIR Spectroscopy and Chemometrics

Variations occurring in coffee beans during roasting are ascribable to several chemical-physical phenomena: to quickly track the whole process and to ensure its reproducibility, a process analytical technology (PAT) approach is needed.

In this study, a method combining in-line Fourier transform near-infrared (FT-NIR) spectroscopy and chemometric modelling was investigated to get real-time and practical knowledge about the roasting effects on coffee’s chemical-physical composition. In-line spectra were acquired by inserting a NIR probe into a laboratory coffee roaster, running twenty-four roasting experiments, planned spanning different coffee species (Arabica and Robusta), four roasting temperature settings (TS1–TS4) and times (650–1580 s).

Multivariate curve resolution-alternate least squares (MCR-ALS) was used to model the chemical-physical changes occurring during the roasting process, and information about maximum rate, acceleration and deceleration of the process was obtained, also highlighting potential effects due to the different roasting temperatures and coffee varieties.

The proposed approach provides the groundwork for direct real-time implementation of rapid, non-invasive automated monitoring of the roasting process at industrial scale.

Discrimination of Robusta Amazônico coffee farmed by indigenous and non-indigenous people in Amazon: comparing benchtop and portable NIR using ComDim and duplex

Robusta Amazônico is the name given to the Amazonian coffee that has been becoming popular and has recently been registered as a geographical indication in Brazil. It is produced by indigenous and non-indigenous coffee producers in regions that are geographically very close to one another. There is a need to authenticate whether coffee is truly produced by indigenous people and near-infrared (NIR) spectroscopy is an excellent technique for this. To meet the substantial trend towards NIR spectroscopy miniaturization, this work compared benchtop and portable NIR instruments to discriminate Robusta Amazônico samples using partial least squares discriminant analysis (PLS-DA). To ensure the results to be fairly comparable and, at the same time, to guarantee representative selection of both training and test set for the discriminant analysis, a sample selection strategy based on coupling ComDim multi-block analysis and the duplex algorithm was applied. Different pre-processing techniques were tested to create multiple matrices to be used in ComDim, as well as to build the discriminant models. The best PLS-DA model for benchtop NIR provided an accuracy of 96% for the test samples, while for the portable NIR the correct classification rate was 92%. It was demonstrated that portable NIR provides similar results to benchtop NIR for coffee origin classification by performing an unbiased sample selection strategy.

In-Line Near-Infrared Spectroscopy Gives Rapid and Precise Assessment of Product Quality and Reveals Unknown Sources of Variation-A Case Study from Commercial Cheese Production

Quality testing in the food industry is usually performed by manual sampling and at/off-line laboratory analysis, which is labor intensive, time consuming, and may suffer from sampling bias. For many quality attributes such as fat, water and protein, in-line near-infrared spectroscopy (NIRS) is a viable alternative to grab sampling. The aim of this paper is to document some of the benefits of in-line measurements at the industrial scale, including higher precision of batch estimates and improved process understanding. Specifically, we show how the decomposition of continuous measurements in the frequency domain, using power spectral density (PSD), may give a useful view of the process and serve as a diagnostic tool. The results are based on a case regarding the large-scale production of Gouda-type cheese, where in-line NIRS was implemented to replace traditional laboratory measurements. In conclusion, the PSD of in-line NIR predictions revealed unknown sources of variation in the process that could not have been discovered using grab sampling. PSD also gave the dairy more reliable data on key quality attributes, and laid the foundation for future improvements.

Quantitative Determination of Diosmin in Tablets by Infrared and Raman Spectroscopy

Diosmin is widely used in the treatment of chronic venous diseases and hemorrhoids. Based on Raman and infrared reflection spectra of powdered tablets in the mid- and near-infrared regions and results of reference high-performance liquid chromatographic analysis, partial least squares models that enable fast and reliable quantification of the studied active ingredient in tablets, without the need for extraction, were elaborated. Eight commercial preparations containing diosmin in the 66-92% (w/w) range were analyzed. In order to assess and compare the quality of the developed chemometric models, the relative standard errors of prediction for calibration and validation sets were calculated. We found these errors to be in the 1.0-2.4% range for the three spectroscopic techniques used. Diosmin content in the analyzed preparations was obtained with recoveries in the 99.5-100.5% range.

A Study of the Reliability and Accuracy of the Real-Time Detection of Forage Maize Quality Using a Home-Built Near-Infrared Spectrometer

The current study was conducted to explore the real-time detection capability of a home-built grating-type near-infrared (NIR) spectroscopy online system to determine forage maize quality. The factor parameters affecting the online NIR spectrum collection were analyzed, and the results indicated that the detection optical path of 12 cm, conveyor speeds of 10 cm s−1, and number of scans of 32 were the optimal parameters. Choosing the crude protein and moisture of forage maize as quality indicators, the reliability of the home-built NIR online spectrometer was confirmed compared with other general research NIR instruments. In addition, an NIR online multivariate analysis model developed using the partial least squares (PLS) method for the prediction of forage maize quality was established, and the reliability, applicability, and stability of the NIR model were further discussed. The results illustrated that the home-built grating-type NIR online system performed satisfying and comparable accuracy and repeatability of the real-time prediction of forage maize quality.

Rapid identification of the geographic origin of Taiping Houkui green tea using near-infrared spectroscopy combined with a variable selection method

BACKGROUND

Most studies focus on the geographically larger production areas in tea traceability. However, famous high-quality tea is often produced in a narrow range of origins, which makes traceability a challenge. In this study, Taiping Houkui (TPHK) green tea of narrow geographical origin was rapidly identified using Fourier-transform near-infrared (FT-NIR) spectroscopy.

RESULTS

First, spectral information of 114 TPHK samples from four production areas was acquired. Second, the synthetic minority over-sampling technique (SMOTE) was used to balance the sample data set, and three different spectral pre-processing methods were compared. Third, three feature variable selection algorithms were used to obtain the pre-processed spectral features. Finally, extreme learning machine (ELM) models based on the variables obtained from the selected features were established to trace the TPHK origin. The optimized ELM model achieves 95.35% classification accuracy in the test set.

CONCLUSION

The present study demonstrates that the optimized variable selection method in combination with NIR spectroscopy represents a suitable strategy for tea traceability in narrow regions. © 2022 Society of Chemical Industry.

Chemometric Differentiation of Sole and Plaice Fish Fillets Using Three Near-Infrared Instruments

Fish species substitution is one of the most common forms of fraud all over the world, as fish identification can be very challenging for both consumers and experienced inspectors in the case of fish sold as fillets. The difficulties in distinguishing among different species may generate a “grey area” in which mislabelling can occur. Thus, the development of fast and reliable tools able to detect such frauds in the field is of crucial importance. In this study, we focused on the distinction between two flatfish species largely available on the market, namely the Guinean sole (Synaptura cadenati) and European plaice (Pleuronectes platessa), which are very similar looking. Fifty fillets of each species were analysed using three near-infrared (NIR) instruments: the handheld SCiO (Consumer Physics), the portable MicroNIR (VIAVI), and the benchtop MPA (Bruker). PLS-DA classification models were built using the spectral datasets, and all three instruments provided very good results, showing high accuracy: 94.1% for the SCiO and MicroNIR portable instruments, and 90.1% for the MPA benchtop spectrometer. The good classification results of the approach combining NIR spectroscopy, and simple chemometric classification methods suggest great applicability directly in the context of real-world marketplaces, as well as in official control plans.

Advances in NIR Spectroscopy Analytical Technology in Food Industries

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Non-destructive assessment of vitamin C in foods: a review of the main findings and limitations of vibrational spectroscopic techniques

The constant increase in the demand for safe and high-quality food has generated the need to develop efficient methods to evaluate food composition, vitamin C being one of the main quality indicators. However, its heterogeneity and susceptibility to degradation makes the analysis of vitamin C difficult by conventional techniques, but as a result of technological advances, vibrational spectroscopy techniques have been developed that are more efficient, economical, fast, and non-destructive. This review focuses on main findings on the evaluation of vitamin C in foods by using vibrational spectroscopic techniques. First, the fundamentals of ultraviolet–visible, infrared and Raman spectroscopy are detailed. Also, chemometric methods, whose use is essential for a correct processing and evaluation of the spectral information, are described. The use and importance of vibrational spectroscopy in the evaluation of vitamin C through qualitative characterization and quantitative analysis is reported. Finally, some limitations of the techniques and potential solutions are described, as well as future trends related to the utilization of vibrational spectroscopic techniques.

Application of NIR Spectral Standardization Based on Principal Component Score Evaluation in Wheat Flour Crude Protein Model Sharing

In order to explore spectral standardization methods for spectra collected by different NIR spectrometers, to reduce spectral differences, and to realize model sharing among different instruments, the crude protein content of 154 wheat flour samples was measured using one grating and three Fabry-Perot tunable filter NIR spectrometers in wavelength. At the same wavelength range and wavelength interval, three algorithms, namely, direct standardization (DS), piecewise direct standardization (PDS), and simple linear regression direct standardization (SLRDS), were used to standardize spectra collected by different instruments from the same samples. Spectral standardization error rate (SSER), principal component score error rate (PCSER), and other indicators were employed to analyze the spectral differences between the master and the target spectra, and the effect of model sharing was evaluated using parameters including prediction correlation coefficient (Rp), root mean square error of prediction (RMSEP), and relative prediction deviation (RPD). The results show the following: (1) The difference between spectra can be quantitatively evaluated through analyzing SSER and PCSER. (2) After standardization by the three algorithms, the spectral difference between the three target and the master spectrometers is significantly reduced and the prediction effect of the master model is greatly improved. (3) Among the three algorithms, DS algorithm had the smallest error rate in standardizing spectra from three target spectrometers. After standardization by the DS algorithm, the master model had the best effect. Its prediction accuracy was greatly improved compared with that before standardization. (4) The standard model established based on the S450 spectrometer can be applied to the same spectrometer as the N500 spectrometer with the same resolution and different wavelength ranges, so as to achieve model sharing. Therefore, DS, PDS, and SLRDS algorithms can effectively reduce the spectral differences between different instruments and realize the sharing of NIR calibration models for wheat flour crude protein measurement.

Consensual Regression of Soluble Solids Content in Peach by Near Infrared Spectrocopy

In order to reduce the uncertainty of the genetic algorithm (GA) in optimizing the near-infrared spectral calibration model and avoid the loss of spectral information of the unselected variables, a strategy of fusing consensus models is proposed to measure the soluble solids content (SSC) in peaches. A total of 266 peach samples were collected at four arrivals, and their interactance spectra were scanned by an integrated analyzer prototype, and then an internal index of SSC was destructively measured by the standard refractometry method. The near-infrared spectra were pre-processed with mean centering and were selected successively with a genetic algorithm (GA) to construct the consensus model, which was integrated with two member models with optimized weightings. One was the conventional partial least square (PLS) optimized with GA selected variables (PLS_GA), and the other one was the derived PLS developed with residual variables after GA selections (PLS_RV). The performance of PLS_RV models showed some useful spectral information related to peaches’ SSC and someone performed close to the full-spectral-based PLS model. Among these 10 runs, consensus models obtained a lower root mean squared errors of prediction (RMSEP), with an average of 1.106% and standard deviation (SD) of 0.0068, and performed better than that of the optimized PLS_GA models, which achieved a RMSEP of average 1.116% with SD of 0.0097. It can be concluded that the application of fusion strategy can reduce the fluctuation uncertainty of a model optimized by genetic algorithm, fulfill the utilization of the spectral information amount, and realize the rapid detection of the internal quality of the peach.

Assessment of Pumpkin Seed Oil Adulteration Supported by Multivariate Analysis: Comparison of GC-MS, Colourimetry and NIR Spectroscopy Data

Because of its high market value, pumpkin seed oil is occasionally adulterated by cheaper refined oils, usually sunflower oil. The standard method for detecting its authenticity is based on expensive and laborious determination of the sterol composition. Therefore, the objective of this study was to determine the sterol content and authenticity of retail oils labelled as pumpkin seed oil and also to investigate the potential of near-infrared spectroscopy (NIR) and colourimetry in detecting adulteration. The results show that due to the significant decrease in Δ7-sterols and increase in Δ5-sterols, 48% of the analysed oils can be declared as adulterated blends of pumpkin seed and sunflower oil. Significant differences in NIR spectroscopy data, in the range of 904-922 nm and 1675-1699 nm, and colourimetric data were found between the control pumpkin seed oil and sunflower oil, but only the NIR method had the potential to detect the authenticity of pumpkin seed oil, which was confirmed by principal component analysis. Orthogonal projection on latent structures (OPLS) discriminant analysis, resulted in working classification models that were able to discriminate pure and adulterated oil. OPLS models based on NIR spectra were also able to successfully predict the content of β-sitosterol and Δ7,22-stigmastadienol in the analysed oils.

A FT-NIR Process Analytical Technology Approach for Milk Renneting Control

The study proposes a process analytical technology (PAT) approach for the control of milk coagulation through near infrared spectroscopy (NIRS), computing multivariate statistical process control (MSPC) charts, based on principal component analysis (PCA). Reconstituted skimmed milk and commercial pasteurized skimmed milk were mixed at two different ratios (60:40 and 40:60). Each mix ratio was prepared in six replicates and used for coagulation trials, monitored by fundamental rheology, as a reference method, and NIRS by inserting a probe directly in the coagulation vat and collecting spectra at two different acquisition times, i.e., 60 s or 10 s. Furthermore, three failure coagulation trials were performed, deliberately changing temperature or rennet and CaCl₂ concentration. The comparison with fundamental rheology results confirmed the effectiveness of NIRS to monitor milk renneting. The reduced spectral acquisition time (10 s) showed data highly correlated (r > 0.99) to those acquired with longer acquisition time. The developed decision trees, based on PC1 scores and T² MSPC charts, confirmed the suitability of the proposed approach for the prediction of coagulation times and for the detection of possible failures. In conclusion, the work provides a robust but simple PAT approach to assist cheesemakers in monitoring the coagulation step in real-time.

Rapid Estimation of Potato Quality Parameters by a Portable Near-Infrared Spectroscopy Device

The aim of the present work was to determine the main quality parameters on tuber potato using a portable near-infrared spectroscopy device (MicroNIR). Potato tubers protected by the Protected Geographical Indication (PGI “Patata de Galicia”, Spain) were analyzed both using chemical methods of reference and also using the NIR methodology for the determination of important parameters for tuber commercialization, such as dry matter and reducing sugars. MicroNIR technology allows for the attainment/estimation of dry matter and reducing sugars in the warehouses by directly measuring the tubers without a chemical treatment and destruction of samples. The principal component analysis and modified partial least squares regression method were used to develop the NIR calibration model. The best determination coefficients obtained for dry matter and reducing sugars were of 0.72 and 0.55, respectively, and with acceptable standard errors of cross-validation. Near-infrared spectroscopy was established as an effective tool to obtain prediction equations of these potato quality parameters. At the same time, the efficiency of portable devices for taking instantaneous measurements of crucial quality parameters is useful for potato processors.

Dry Powder Mixing Is Feasible in Continuous Twin Screw Extruder: Towards Lean Extrusion Process for Oral Solid Dosage Manufacturing

Using discrete element method (DEM) modeling and near-infrared (NIR) spectroscopy, the feasibility of powder mixing in the initial pre-melting zones of a twin screw extruder using two independent feeders was studied. Previous work in the pharmaceutical and food industry has focused on mixing when materials are melted or on material homogeneity at the extruder's output. Depending on the formulation, ensuring a fully blended formulation prior to melting may be desired. Experiments were conducted using a Coperion ZSK-18 extruder to evaluate if blend uniformity can be achieved by exploring screw configuration, screw speed, and powder feed rate. As powder exited the extruder and deposited on a conveyor belt, an in-line NIR spectrophotometer measured spectra of material. Chemometric-based models predicted unknown concentrations to evaluate if blend uniformity was achieved. Using the EDEM software, Hertz-Mindlin contact model, and dimensions of the extruder, DEM simulations complemented the experimental work. The DEM computational models provided understanding of mixing patterns inside the extruder at particle scale and helped select the screw configuration before doing experimentation. The simulations showed good axial mixing for all the screw configurations studied, while good cross (radial) mixing was only observed for the screw configuration with 90-degree kneading elements. Therefore, the screw configuration with two 90-degree kneading elements was chosen for the experimental study. The RTD profiles when using a screw configuration with only conveying screw elements are comparable to a plug flow reactor (PFR), while the profiles when using kneading elements are more comparable to an ideal continuous stirred tank reactor (CSTR). For the screw configuration with 90 degrees kneading elements, the mean residence time (MRT) decreases with an increase in the screw speed. Experimental NIR spectra showed that concentrations can be predicted with an error of 2%. It was demonstrated that the twin screw extruder can provide proper dry powder mixing of two powder feed streams based on a unit dose scale, enabling continuous powder mixing prior to the melting zone in the extruder for the formulation studied with a cohesive API. This setup may also work for other types of formulations. These studies can help in developing lean hot melt as well as wet extrusion/granulation processes using twin screw extruders for the continuous manufacturing of oral solid dosage products.

Application of Artificial Intelligence in Food Industry—a Guideline

Artificial intelligence (AI) has embodied the recent technology in the food industry over the past few decades due to the rising of food demands in line with the increasing of the world population. The capability of the said intelligent systems in various tasks such as food quality determination, control tools, classification of food, and prediction purposes has intensified their demand in the food industry. Therefore, this paper reviews those diverse applications in comparing their advantages, limitations, and formulations as a guideline for selecting the most appropriate methods in enhancing future AI- and food industry–related developments. Furthermore, the integration of this system with other devices such as electronic nose, electronic tongue, computer vision system, and near infrared spectroscopy (NIR) is also emphasized, all of which will benefit both the industry players and consumers.

The application of parallel processing in the selection of spectral variables in beer quality control

Parallel data analysis was investigated to improve performance in variable selection and to develop predictive models for beer quality control. A set of spectral near infrared (NIR) data from 60 beer samples and its primitive extracts as the original concentration was used. The dataset was distributed to Raspberry Pi 3 Model B devices connected to a network that was running a Machine Learning service. With more than 4 devices acting in parallel, it was possible to reduce time in 57% to find the best linear regression coefficient (0.999) with the lower RMSECV (0.216) if compared to a singular desktop computer. Thus, parallel processing can significantly reduce the time to indicate the best model fitted during the variable's selection.

Assessment of the Analytical Performance of Three Near-Infrared Spectroscopy Instruments (Benchtop, Handheld and Portable) through the Investigation of Coriander Seed Authenticity

The performance of three near-infrared spectroscopy (NIRS) instruments was compared through the investigation of coriander seed authenticity. The Thermo Fisher iS50 NIRS benchtop instrument, the portable Ocean Insights Flame-NIR and the Consumer Physics handheld SCiO device were assessed in conjunction with chemometric modelling in order to determine their predictive capabilities and use as quantitative tools through regression analysis. Two hundred authentic coriander seed samples and ninety adulterated samples were analysed on each device. Prediction models were developed and validated using SIMCA 15 chemometric software. All instruments correctly predicted 100% of the adulterated samples. The best models resulted in correct predictions of 100%, 98.5% and 95.6% for authentic coriander samples using spectra from the iS50, Flame-NIR and SCiO, respectively. The development of regression models highlighted the limitations of the Flame-NIR and SCiO for quantitative analysis, compared to the iS50. However, the results indicate their use as screening tools for on-site analysis of food, at various stages of the food supply chain.

Optimization of Instrument Design for In-Line Monitoring of Dry Matter Content in Single Potatoes by NIR Interaction Spectroscopy

Dry matter (DM) content is one of the most important quality features of potatoes. It defines the physical properties of the potatoes and determines what kind of product the potatoes can be used for. This paper presents the results obtained by a novel prototype NIR (near-infrared) instrument designed to measure DM content in single potatoes in process. The instrument is based on interaction measurements to measure deeper into the potatoes. It measures rapidly, up to 50 measurements per second, allowing several moving potatoes to be measured per second. The instrument also enables several interactance distances to be recorded for each measurement. The instrument was calibrated based on three different potato varieties and the calibration measurements were done in a process plant, making the calibration model suitable for in-line use. A good calibration for DM was obtained by partial least squares regression (RMSECV = 0.78% DM, R² = 0.91). The instrument was tested in-line in the process plant and several batches of potatoes were monitored for the estimation of the DM distribution per batch. Accuracy of DM determination as function of measurement position on the potato was studied, and results indicate that NIR scans along the center part of the potatoes give slightly better results compared to scans taken on either side of the center. Small differences in optical measurement geometry influence the accuracy of the calibration models, underlining the importance of optimizing instrument design for successful measurements.

Multi-Way Analysis Coupled with Near-Infrared Spectroscopy in Food Industry: Models and Applications

Near-infrared spectroscopy (NIRS) is a fast and powerful analytical tool in the food industry. As an advanced chemometrics tool, multi-way analysis shows great potential for solving a wide range of food problems and analyzing complex spectroscopic data. This paper describes the representative multi-way models which were used for analyzing NIRS data, as well as the advances, advantages and limitations of different multi-way models. The applications of multi-way analysis in NIRS for the food industry in terms of food process control, quality evaluation and fraud, identification and classification, prediction and quantification, and image analysis are also reviewed. It is evident from this report that multi-way analysis is presently an attractive tool for modeling complex NIRS data in the food industry while its full potential is far from reached. The combination of multi-way analysis with NIRS will be a promising practice for turning food data information into operational knowledge, conducting reliable food analyses and improving our understanding about food systems and food processes. To the best of our knowledge, this is the first paper that systematically reports the advances on models and applications of multi-way analysis in NIRS for the food industry.

Spoilage assessment of chicken breast fillets by means of fourier transform infrared spectroscopy and multispectral image analysis

The objective of this research was the evaluation of Fourier transforms infrared spectroscopy (FT-IR) and multispectral image analysis (MSI) as efficient spectroscopic methods in tandem with multivariate data analysis and machine learning for the assessment of spoilage on the surface of chicken breast fillets. For this purpose, two independent storage experiments of chicken breast fillets (n ​= ​215) were conducted at 0, 5, 10, and 15 ​°C for up to 480 ​h. During storage, samples were analyzed microbiologically for the enumeration of Total Viable Counts (TVC) and Pseudomonas spp. In addition, FT-IR and MSI spectral data were collected at the same time intervals as for microbiological analyses. Multivariate data analysis was performed using two software platforms (a commercial and a publicly available developed platform) comprising several machine learning algorithms for the estimation of the TVC and Pseudomonas spp. population of the surface of the samples. The performance of the developed models was evaluated by intra batch and independent batch testing. Partial Least Squares- Regression (PLS-R) models from the commercial software predicted TVC with root mean square error (RMSE) values of 1.359 and 1.029 log CFU/cm² for MSI and FT-IR analysis, respectively. Moreover, RMSE values for Pseudomonas spp. model were 1.574 log CFU/cm² for MSI data and 1.078 log CFU/cm² for FT-IR data. From the implementation of the in-house sorfML platform, artificial neural networks (nnet) and least-angle regression (lars) were the most accurate models with the best performance in terms of RMSE values. Nnet models developed on MSI data demonstrated the lowest RMSE values (0.717 log CFU/cm²) for intra-batch testing, while lars outperformed nnet on independent batch testing with RMSE of 1.252 log CFU/cm². Furthermore, lars models excelled with the FT-IR data with RMSE of 0.904 and 0.851 log CFU/cm² in intra-batch and independent batch testing, respectively. These findings suggested that FT-IR analysis is more efficient than MSI to predict the microbiological quality on the surface of chicken breast fillets.

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Poultry meat’s vulnerability to spoilage demands rapid quality assessment LWT-Food Sci. Technol.methods.

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FT-IR and MSI are non-invasive methods applied in a variety of meat products.

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SorfML is a web platform providing diverse machine learning algorithms.

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FT-IR analysis via lars predicted efficiently microbial loads of TVC.

Reduction of the Number of Samples for Cost-Effective Hyperspectral Grape Quality Predictive Models

Developing chemometric models from near-infrared (NIR) spectra requires the use of a representative calibration set of the entire population. Therefore, generally, the calibration procedure requires a large number of resources. For that reason, there is a great interest in identifying the most spectrally representative samples within a large population set. In this study, principal component and hierarchical clustering analyses have been compared for their ability to provide different representative calibration sets. The calibration sets generated have been used to control the technological maturity of grapes and total phenolic compounds of grape skins in red and white cultivars. Finally, the accuracy and precision of the models obtained with these calibration sets resulted from the application of the selection algorithms studied have been compared with each other and with the whole set of samples using an external validation set. Most of the standard errors of prediction (SEP) in external validation obtained from the reduced data sets were not significantly different from those obtained using the whole data set. Moreover, sample subsets resulting from hierarchical clustering analysis appear to produce slightly better results.

N-Way NIR Data Treatment through PARAFAC in the Evaluation of Protective Effect of Antioxidants in Soybean Oil

The use of chemometric tools is progressing to scientific areas where analytical chemistry is present, such as food science. In analytical food evaluation, oils represent an important field, allowing the exploration of the antioxidant effects of herbs and seeds. However, traditional methodologies have some drawbacks which must be overcome, such as being time-consuming, requiring sample preparation, the use of solvents/reagents, and the generation of toxic waste. The objective of this study is to evaluate the protective effect provided by plant-based substances (directly, or as extracts), including pumpkin seeds, poppy seeds, dehydrated goji berry, and Provençal herbs, against the oxidation of antioxidant-free soybean oil. Synthetic antioxidants tert-butylhydroquinone and butylated hydroxytoluene were also considered. The evaluation was made through thermal degradation of soybean oil at different temperatures, and near-infrared spectroscopy was employed in an n-way mode, coupled with Parallel Factor Analysis (PARAFAC) to extract nontrivial information. The results for PARAFAC indicated that factor 1 shows oxidation product information, while factor 2 presents results regarding the antioxidant effect. The plant-based extract was more effective in improving the frying stability of soybean oil. It was also possible to observe that while the oxidation product concentration increased, the antioxidant concentration decreased as the temperature increased. The proposed method is shown to be a simple and fast way to obtain information on the protective effects of antioxidant additives in edible oils, and has an encouraging potential for use in other applications.

Monitoring the Processing of Dry Fermented Sausages with a Portable NIRS Device

This work studies the ability of a MicroNIR (VIAVI, Santa Rosa, CA) device to monitor the dry fermented sausage process with the use of multivariate data analysis. Thirty sausages were made and subjected to dry fermentation, which was divided into four main stages. Physicochemical (weight lost, pH, moisture content, water activity, color, hardness, and thiobarbiruric reactive substances analysis) and sensory (quantitative descriptive analysis) characterizations of samples on different steps of the ripening process were performed. Near-infrared (NIR) spectra (950-1650 nm) were taken throughout the process at three points of the samples. Physicochemical data were explored by distance to K-Nearest Neighbor (K-NN) cluster analysis, while NIR spectra were studied by partial least square-discriminant analysis; before these models, Principal Component Analysis (PCA) was performed in both databases. The results of multivariate data analysis showed the ability to monitor and classify the different stages of ripening process (mainly the fermentation and drying steps). This study showed that a portable NIR device (MicroNIR) is a nondestructive, simple, noninvasive, fast, and cost-effective tool with the ability to monitor the dry fermented sausage processing and to classify samples as a function of the stage, constituting a feasible decision method for sausages to progress to the following processing stage.