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

Meno, Laura; Rodríguez-Flores, María Shantal; Seijo, María Carmen

doi:10.3390/s21248222

Cited by 15 publications

(16 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the other hand, the model developed for palmitic, linolenic, and eicosenoic acids were moderately suitable with R 2 value of 0.68, 0.77, and 0.74 respectively. These were in agreement to NIRS models developed for tryptophan in maize 30 and fibers in feeds 23 that reported by Escuredo et al 31 The RPD C values of calibration equations developed for oil, palmitic, stearic, oleic, linoleic, and erucic acids are more than two as shown in Table 2, which were acceptable result in the good range as studied by Aldo et al 32 Furthermore, the RPD C values for palmitic, linolenic, and eicosenoic acid were 1.6, 1.8, and 1.7, respectively, which were lower than the other equations but still adequate for screening, according to Khamchum et al 33 RER values of all equation of this study varied from 10.0 to 23.0 which agreed with the recommended values for screening purposes as reported by Yang et al 10 except models developed for palmitic, linolenic, and eicosenoic acid.…”

Section: Calibration and Cross-validation Of Nirs Modelsupporting

confidence: 92%

Development of non‐destructive models to predict oil content and fatty acid composition of Gomenzer (Ethiopian mustard) using near‐infrared reflectance spectroscopy

et al. 2023

View full text Add to dashboard Cite

Background ‟Gomenzer” or Ethiopian mustard is the potential oilseed crop that is economically important. Ethiopian mustard and rapeseed quality breeding has focused on altering the fatty acid contents of seed oil to create novel genotypes with different oil characteristics. The goal of this research was to establish calibration equations' using a method called near‐infrared reflectance spectroscopy with modified partial least squares (MPLS) regression. Result The spectra of 180 mustard samples were collected and their oil and fatty acid compositions were determined by n‐hexane extraction and GC–MS methods respectively. With 130 samples, calibration equations were developed for oil and fatty acid compositions. All developed equation had an acceptable value of R 2c, RPDc, and also had suitable 1‐VR values. Prediction of an external validation with 50 datasets revealed a blameless correlation between reference values and NIRS values based on the R 2v in prediction, SEP, and the ratio of SD to SEP (RPDv) for oil, oleic, linoleic, linolenic, and erucic acids. These had suitable values of RPDv and R 2v, with the range of 2.0 to 8.8 and 0.79 to 0.99 respectively. Conclusion The developed models gave meaningful quantitative data, however, the equations generated for palmitic, stearic, and eicosenoic acids were insufficient for data analysis. Hence, the developed NIRS model, which was satisfactory, could be used to assess and evaluate the oil and fatty acid content for the unknown mustard samples on a regular basis in the oilseed breeding quality program.

show abstract

Section: Calibration and Cross-validation Of Nirs Modelsupporting

confidence: 92%

Development of non‐destructive models to predict oil content and fatty acid composition of Gomenzer (Ethiopian mustard) using near‐infrared reflectance spectroscopy

et al. 2023

View full text Add to dashboard Cite

show abstract

“…[10][11][12][13] Despite quite widespread use of spectroscopic techniques in the analysis of food and natural products, this type of analysis of potatoes and products derived from them is not common. NIR spectroscopy has been applied to assess quality parameters of potatoes, including dry matter, reducing sugars and polyphenol content analysis, 14,15 and to discriminate powdered, pure and adulterated sweet potato samples. 16 Raman spectroscopy has been applied to identify nine potato varieties and to determine their origin, 17 and together with infrared techniques it has been used to determine the composition of potato chips.…”

Section: Introductionmentioning

confidence: 99%

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

Pielorz,

Kita,

Rytel

et al. 2023

J Sci Food Agric

View full text Add to dashboard Cite

BACKGROUNDDetermination 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.RESULTSFluorescence spectroscopy and three vibrational spectroscopy techniques were applied to determine total polyphenols content, antioxidant activity and macronutrient levels in red‐ and purple‐fleshed potato varieties. Excitation‐emission matrix (EEM) fluorescence, FT Raman, attenuated total reflection infrared (FTIR ATR) and near‐infrared (NIR) spectra were recorded for the freeze dried samples. Combining spectral data and the results of reference analyses, partial least squares regression (PLS) 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 sugars determinations, respectively.CONCLUSIONThe 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 spectra. They can effectively replace commonly used protocols of food product analysis requiring sample dissolving and extraction of the compounds of interest.This article is protected by copyright. All rights reserved.

show abstract

“…Some studies have been conducted to test the near-infrared spectroscopy to measure quality parameters of potatoes, such as sugars or dry matter content [ 31 , 32 , 33 , 34 ]. Escuredo et al [ 35 ] estimated potato quality parameters using a portable near-infrared spectrometer. They used principal component analysis and modified partial least squares regression method to develop the NIR calibration model.…”

Section: Introductionmentioning

confidence: 99%

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

Khorramifar

Sharabiani

Karami

et al. 2022

Foods

View full text Add to dashboard Cite

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.

show abstract

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

Cited by 15 publications

References 35 publications

Development of non‐destructive models to predict oil content and fatty acid composition of Gomenzer (Ethiopian mustard) using near‐infrared reflectance spectroscopy

Development of non‐destructive models to predict oil content and fatty acid composition of Gomenzer (Ethiopian mustard) using near‐infrared reflectance spectroscopy

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

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

Contact Info

Product

Resources

About