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

Relationship between optical properties and internal quality of potatoes during storage

The optical properties [absorption coefficient (μa) and reduced scattering coefficient (μs')] and internal quality [firmness (FI), moisture content (MC), and soluble solids content (SSC)] of stored potatoes at 25 °C were determined, along with ultrastructure observation. Potato tissue ultrastructure changed significantly with storage time, exhibiting enhanced scattering properties and a monotonic increase in μs'. The μa spectra showed significant correlations with MC and SSC, while the μs' spectra were more strongly correlated with FI. The competitive adaptive reweighted sampling (CARS) algorithm improved the prediction accuracy for partial least squares regression (PLSR) and support vector regression (SVR) models. The best predictions were 1st-Derivative-μs'-FI-PLSR (RP = 0.897, RMSEP = 0.036 N, RPD = 2.262), SG-μa -MC-SVR (RP = 0.886, RMSEP = 0.438 %, RPD = 2.157), and Raw-μa -SSC-SVR (RP = 0.873, RMSEP = 0.137 %, RPD = 2.050). These results demonstrate the potential for predicting internal quality using potato's optical properties.

Development and Validation of Near-Infrared Reflectance Spectroscopy Prediction Modeling for the Rapid Estimation of Biochemical Traits in Potato

Potato is a globally significant crop, crucial for food security and nutrition. Assessing vital nutritional traits is pivotal for enhancing nutritional value. However, traditional wet lab methods for the screening of large germplasms are time- and resource-intensive. To address this challenge, we used near-infrared reflectance spectroscopy (NIRS) for rapid trait estimation in diverse potato germplasms. It employs molecular absorption principles that use near-infrared sections of the electromagnetic spectrum for the precise and rapid determination of biochemical parameters and is non-destructive, enabling trait monitoring without sample compromise. We focused on modified partial least squares (MPLS)-based NIRS prediction models to assess eight key nutritional traits. Various mathematical treatments were executed by permutation and combinations for model calibration. The external validation prediction accuracy was based on the coefficient of determination (RSQexternal), the ratio of performance to deviation (RPD), and the low standard error of performance (SEP). Higher RSQexternal values of 0.937, 0.892, and 0.759 were obtained for protein, dry matter, and total phenols, respectively. Higher RPD values were found for protein (3.982), followed by dry matter (3.041) and total phenolics (2.000), which indicates the excellent predictability of the models. A paired t-test confirmed that the differences between laboratory and predicted values are non-significant. This study presents the first multi-trait NIRS prediction model for Indian potato germplasm. The developed NIRS model effectively predicted the remaining genotypes in this study, demonstrating its broad applicability. This work highlights the rapid screening potential of NIRS for potato germplasm, a valuable tool for identifying trait variations and refining breeding strategies, to ensure sustainable potato production in the face of climate change.

Causal factors concerning the texture of French fries manufactured at industrial scale

In this paper, we review the physical/chemical phenomena, contributing to the final texture of French fries, as occurs in the whole industrial production chain of frozen par-fried fries. Our discussion is organized following a multiscale hierarchy of these causal factors, where we distinguish the molecular, cellular, microstructural, and product levels. Using the same multiscale framework, we also discuss currently available theoretical knowledge, and experimental methods probing the relevant physical/chemical phenomena. We have identified knowledge gaps, and experimental methods are evaluated in terms of the effort and value of their results. With our overviews, we hope to give promising research directions such to arrive at a multiscale model, encompassing all causal factors relevant to the final texture. This multiscale model is the ultimate tool to evaluate process innovations for effects on final textural quality, which can be balanced against the impacts on sustainability and economics.

Application of vibrational and fluorescence spectroscopy to the compositional analysis of colored-flesh potatoes

BACKGROUND

Determination of composition and physicochemical parameters of natural products requires dedicated, often laborious and expensive, analytical protocols. Different spectroscopic techniques, in conjunction with chemometrics, seem to have a considerable potential in direct analysis of raw plant material and foods, without any chemical treatment.

RESULTS

Fluorescence spectroscopy and three vibrational spectroscopy techniques were applied to determine total polyphenol content, antioxidant activity and macronutrient levels in red- and purple-fleshed potato varieties. Excitation-emission matrix fluorescence, Fourier transform Raman, attenuated total reflection Fourier transform infrared and near-infrared spectra were recorded for the freeze-dried samples. Combining spectral data and the results of reference analyses, partial least squares regression models were constructed for each parameter studied. For polyphenols and antioxidant activity, quantification errors found for validation samples amounted to 3.74-5.04% and 4.75-6.35%, respectively, whereas macronutrient analysis gave errors in the 3.45-4.55%, 3.09-5.30% and 5.10-8.58% ranges for starch, protein and sugar determinations, respectively.

CONCLUSION

The obtained results demonstrate that different spectroscopic techniques in combination with multivariate modeling allow simultaneous determination of various parameters of plant samples based on a single sample spectrum. They can effectively replace commonly used protocols of food product analysis requiring sample dissolving and extraction of the compounds of interest. © 2023 Society of Chemical Industry.

Portable NIR spectroscopy: the route to green analytical chemistry

There is a growing interest for cost-effective and nondestructive analytical techniques in both research and application fields. The growing approach by near-infrared spectroscopy (NIRs) pushes to develop handheld devices devoted to be easily applied for in situ determinations. Consequently, portable NIR spectrometers actually result definitively recognized as powerful instruments, able to perform nondestructive, online, or in situ analyses, and useful tools characterized by increasingly smaller size, lower cost, higher robustness, easy-to-use by operator, portable and with ergonomic profile. Chemometrics play a fundamental role to obtain useful and meaningful results from NIR spectra. In this review, portable NIRs applications, published in the period 2019-2022, have been selected to indicate starting references. These publications have been chosen among the many examples of the most recent applications to demonstrate the potential of this analytical approach which, not having the need for extraction processes or any other pre-treatment of the sample under examination, can be considered the "true green analytical chemistry" which allows the analysis where the sample to be characterized is located. In the case of industrial processes or plant or animal samples, it is even possible to follow the variation or evolution of fundamental parameters over time. Publications of specific applications in this field continuously appear in the literature, often in unfamiliar journal or in dedicated special issues. This review aims to give starting references, sometimes not easy to be found.

Investigating Changes in pH and Soluble Solids Content of Potato during the Storage by Electronic Nose and Vis/NIR Spectroscopy

Potato is an important agricultural product, ranked as the fourth most common product in the human diet. Potato can be consumed in various forms. As customers expect safe and high-quality products, precise and rapid determination of the quality and composition of potatoes is of crucial significance. The quality of potatoes may alter during the storage period due to various phenomena. Soluble solids content (SSC) and pH are among the quality parameters experiencing alteration during the storage process. This study is thus aimed to assess the variations in SSC and pH during the storage of potatoes using an electronic nose and Vis/NIR spectroscopic techniques with the help of prediction models including partial least squares (PLS), multiple linear regression (MLR), principal component regression (PCR), support vector regression (SVR) and an artificial neural network (ANN). The variations in the SSC and pH are ascending and significant. The results also indicate that the SVR model in the electronic nose has the highest prediction accuracy for the SSC and pH (81, and 92%, respectively). The artificial neural network also managed to predict the SSC and pH at accuracies of 83 and 94%, respectively. SVR method shows the lowest accuracy in Vis/NIR spectroscopy while the PLS model exhibits the best performance in the prediction of the SSC and pH with respective precision of 89 and 93% through the median filter method. The accuracy of the ANN was 85 and 90% in the prediction of the SSC and pH, respectively.

Handheld NIR Spectral Sensor Module Based on a Fully-Integrated Detector Array

For decades, near-infrared (NIR) spectroscopy has been a valuable tool for material analysis in a variety of applications, ranging from industrial process monitoring to quality assessment. Traditional spectrometers are typically bulky, fragile and expensive, which makes them unsuitable for portable and in-field use. Thus, there is a growing interest for miniaturized, robust and low-cost NIR sensors. In this study, we demonstrate a handheld NIR spectral sensor module, based on a fully-integrated multipixel detector array, sensitive in the 850-1700 nm wavelength range. Differently from a spectrometer, the spectral sensor measures a limited number of NIR spectral bands. The capabilities of the spectral sensor module were evaluated alongside a commercially available portable spectrometer for two application cases: to quantify the moisture content in rice grains and to classify plastic types. Both devices achieved the two sensing tasks with comparable performance. Moisture quantification was achieved with a root mean square error (RMSE) prediction of 1.4% and 1.1% by the spectral sensor and spectrometer, respectively. Classification of the plastic type was achieved with a prediction accuracy on unknown samples of 100% and 96.4% by the spectral sensor and spectrometer, respectively. The results from this study are promising and demonstrate the potential for the compact NIR modules to be used in a variety of NIR sensing applications.

Using Vis-NIR Spectroscopy for Predicting Quality Compounds in Foods

Over the past four decades, near-infrared reflectance spectroscopy (NIRS) has become one of the most attractive and used technique for analysis as it allows for fast and simultaneous qualitative and quantitative characterization of a wide variety of food samples [...].