Preliminary study non-destructive sorting techniques for pepper (Capsicum annuum L.) using odor parameter

Rasekh, Mansour; Karami, Hamed; Fuentes, Sigfredo; Kaveh, Mohammad; Rusinek, Robert; Gancarz, Marek

doi:10.1016/j.lwt.2022.113667

Cited by 30 publications

(14 citation statements)

References 48 publications

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“…These include the following: maximum sensor response ΔR/R max , response time t IM , which is measured from the start of the analysis to the achievement of the maximum sensor response, and cleaning time t CL , i.e., the time of removal of the odor from the sensor, which is measured from the end of the analysis to half of the ΔR/R max value. The established parameters depended on the type of the volatile substances and the intensity of the emission of compounds contained in the odor profile [ 42 , 43 ].…”

Section: Methodsmentioning

confidence: 99%

How to Identify Roast Defects in Coffee Beans Based on the Volatile Compound Profile

et al. 2022

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The aim of this study was to detect and identify the volatile compounds in coffee that was obtained in defect roast processes versus standard roasting and to determine the type and strength of the correlations between the roast defects and the volatile compound profile in roasted coffee beans. In order to achieve this goal, the process of coffee bean roasting was set to produce an underdeveloped coffee defect, an overdeveloped coffee defect, and defectless coffee. The “Typica” variety of Arabica coffee beans was used in this study. The study material originated from a plantation that is located at an altitude of 1400–2000 m a.s.l. in Huehuetenango Department, Guatemala. The analyses were carried out with the use of gas chromatography/mass spectrometry (GC–MS) and an electronic nose. This study revealed a correlation between the identified groups of volatile compounds and the following coffee roasting parameters: the time to the first crack, the drying time, and the mean temperatures of the coffee beans and the heating air. The electronic nose helped to identify the roast defects.

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Section: Methodsmentioning

confidence: 99%

How to Identify Roast Defects in Coffee Beans Based on the Volatile Compound Profile

et al. 2022

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“…PLSR, also known as PLS, is a new method of multivariate statistical analysis commonly used for developing multidimensional calibration models. PLSR can process linear data and reduce the number of calibration samples required, making it a gold standard in chemometric analyses [ 3 ]. PLS is a stationary linear regression technique (of Y = AX + B type) that reduces the size of variables by extracting linear combinations from the original sample (X).…”

Section: Methodsmentioning

confidence: 99%

“…Regarding the high expectation of the customers for the safety and quality of the food products, precise, rapid, and targeted determinations of the properties of food products are of crucial significance [ 3 , 4 ]. In the case of potatoes, evaluation of the quality after harvest and sorting is highly important for presenting a reliable and marketable product as the ripening and quality of potatoes are not uniform in the harvesting step [ 5 ].…”

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

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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.

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“…The detection of such counterfeits is usually controlled by document control as required, for example, in the PDO (Protected Designation of Origin) regulations. However, analytical research in the agri-food field is developing methods which compare authentic and suspect samples, using several analytical [ 12 , 13 , 14 ] and statistical methods [ 15 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

A Green Analytical Method Combined with Chemometrics for Traceability of Tomato Sauce Based on Colloidal and Volatile Fingerprinting

et al. 2022

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Tomato sauce is a world famous food product. Despite standards regulating the production of tomato derivatives, the market suffers frpm fraud such as product adulteration, origin mislabelling and counterfeiting. Methods suitable to discriminate the geographical origin of food samples and identify counterfeits are required. Chemometric approaches offer valuable information: data on tomato sauce is usually obtained through chromatography (HPLC and GC) coupled to mass spectrometry, which requires chemical pretreatment and the use of organic solvents. In this paper, a faster, cheaper, and greener analytical procedure has been developed for the analysis of volatile organic compounds (VOCs) and the colloidal fraction via multivariate statistical analysis. Tomato sauce VOCs were analysed by GC coupled to flame ionisation (GC-FID) and to ion mobility spectrometry (GC-IMS). Instead of using HPLC, the colloidal fraction was analysed by asymmetric flow field-fractionation (AF4), which was applied to this kind of sample for the first time. The GC and AF4 data showed promising perspectives in food-quality control: the AF4 method yielded comparable or better results than GC-IMS and offered complementary information. The ability to work in saline conditions with easy pretreatment and no chemical waste is a significant advantage compared to environmentally heavy techniques. The method presented here should therefore be taken into consideration when designing chemometric approaches which encompass a large number of samples.

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Preliminary study non-destructive sorting techniques for pepper (Capsicum annuum L.) using odor parameter

Cited by 30 publications

References 48 publications

How to Identify Roast Defects in Coffee Beans Based on the Volatile Compound Profile

How to Identify Roast Defects in Coffee Beans Based on the Volatile Compound Profile

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

A Green Analytical Method Combined with Chemometrics for Traceability of Tomato Sauce Based on Colloidal and Volatile Fingerprinting

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