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Öğe A comprehensive study on the quality characteristics of sweet red pepper paste and the evaluation of near-infrared spectroscopy as a rapid alternative tool(Springer Science and Business Media Deutschland GmbH, 2023) Ayvaz, Hüseyin; Temizkan, Rıza; Menevşeoğlu, Ahmed; Doğan, Muhammed Ali; Nazlım, Burak Alptuğ; Günay, Ezgi; Pala, Ciğdem UysalRed pepper (Capsicum annuum L. cv. Capia) is an important vegetable obtained from a 1-year cultivated plant belonging to the Solanaceae family. Among the products of red pepper, its paste is very similar to tomato paste in appearance; however, it differs in terms of flavor properties. The wide-ranging quality parameters (dry matter, soluble solids, pH, titration acidity, salt, insoluble ash, total reducing sugars, color values, total carotenoid, antioxidant properties, browning index, and 5-hydroxymethylfurfural) of commercial sweet pepper pastes (n = 52) were determined. To the best of our knowledge, this study is the most comprehensive study on determining the quality parameters of red pepper paste, and its in-depth results will help food authorities issue registrations related to commercial pepper paste in countries such as Türkiye. Additionally, near-infrared (NIR) spectroscopy-based machine-learning algorithms were evaluated for the first time in paste samples as a rapid alternative or complementary to the conventional analytical methods. Regarding the conditional entropy-based models, successful predictions with high accuracy were developed for all the quality parameters of pepper paste. Except for the coefficient of determination value of 0.69 and 0.85 in the prediction of HMF and a/b color values in the developed models, respectively, 0.96 or higher coefficients of determination were obtained for all other parameters. Therefore, NIR spectroscopy, along with the conditional entropy-based predictions, enables simple, fast, multiparametric and accurate quantitative prediction of the numerous quality parameters for commercial sweet pepper pastes and has the potential to be used in routine quality control analyses by pepper paste producers.Öğe Detecting carob powder adulteration in cocoa using near and mid-infrared spectroscopy: A comprehensive classification and regression analysis(Elsevier, 2025) Turgut, Sebahattin Serhat; Ayvaz, Hüseyin; Doğan, Muhammed Ali; Marin, Dolores Perez; Menevşeoğlu, AhmedCocoa powder is a globally traded food product, primarily produced in developing nations, with substantial economic importance. However, it is susceptible to adulteration with inexpensive materials such as carob flour, particularly in low concentrations too low to be detected by sensory methods. To address this issue, rapid analytical techniques such as vibrational spectroscopy combined with multivariate analysis could be beneficial for rapid and reliable detection of adulteration. In this study, spectral data were collected using four different infrared spectrometers: a benchtop FT-NIR system, two portable NIR instruments, and a benchtop FT-MIR-ATR. Samples included pure cocoa, pure carob, and their mixtures with carob concentrations ranging from 0 % to 60 %. Both classification and regression models were developed to detect and quantify the presence of carobs in cocoa powder. Classification models, including Random Forest (RF), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), k-Nearest Neighbors (kNN), Linear Discriminant Analysis (LDA), and Soft Voting Classifiers, demonstrated superior performance for discriminating between cocoa powder, carob powder, and cocoa-carob mixtures with the area under the receiver operating characteristic curve (AUC) scores achieving the level of higher than 0.99, particularly using the benchtop FT-NIR, one of the cost-effective portable NIR and FT-MIR-ATR devices. Similarly, regression models - RF, SVM, MLP, kNN, Partial Least Squares Regression, and Voting Regressor- exhibited robust predictive capabilities. Particularly, FT-MIR and portable NIR based models showed exceptional accuracy with RPD values exceeding 16 and 13, respectively, signifying their applicability in quality and process control. Key wavelength driving model predictions were identified using permutation feature importance for both regressors and classifiers. Overall, these findings highlight and prove the potential of NIR and MIR spectroscopy as rapid, robust, and non-destructive tools for screening and quality control in food authentication.Öğe EZİNE ESKİ KAŞAR PEYNİRİNİN KARAKTERİSTİK BAZI ÖZELLİKLERİ(2019) Doğan, Muhammed Ali; Karagül Yüceer, YoncaÇalışmada, Ezine ve Bayramiç ilçelerinde bulunan mandıralarda üretilen Ezine eski kaşar peynirlerinin bazıfiziksel, kimyasal ve duyusal özelliklerinin belirlenmesi amaçlanmıştır. Peynirlerin genel bileşiminin farklıolduğu, renk ölçümleri ve toplam serbest aminoasit, hidrolitik ransidite ve olgunlaşma düzeyleri arasında dafarklılıklar olduğu görülmüştür. Özellikle, peynirlerin sertlik ve sakızımsılık ölçümleri arasında genişvaryasyon olduğu saptanmıştır. Peynirlerde belirlenen karakteristik bazı duyusal terimleri pişmiş, peyniraltısuyu, kremamsı, sülfür, ransit, tuzlu ve umamidir. Uçucu bileşen kompozisyonunun belirlenmesi için altıpeynir örneği seçilmiştir. Aldehitler, ketonlar, alkoller, asitler ve esterler analiz edilen peynirlerde belirlenenyaygın uçucu bileşenlerdir.Öğe Ezine eski kaşar peynirinin karakteristik bazı özelliklerinin belirlenmesi(Çanakkale Onsekiz Mart Üniversitesi, 2018) Doğan, Muhammed Ali; Yüceer, Yonca KaragülÇalışmada, Çanakkale iline bağlı Ezine ve Bayramiç ilçelerinde bulunan mandıralarda üretilen Ezine Eski Kaşar peynirlerinin bazı fiziksel, kimyasal ve duyusal özelliklerinin belirlenmesi amaçlanmıştır. Renk ve tekstürel özellikleri belirlendikten sonra peynir örneklerinin bileşimi, azot fraksiyonları, toplam serbest aminoasit ve hidrolitik ransidite düzeyleri saptanmıştır. Peynirlerdeki uçucu bileşenler katı faz mikroekstraksiyon tekniği ile izole edilmiş ve Gaz Kromatografisi Kütle Spektrometrisi ile tanımlanmıştır. Örneklerin duyusal özellikleri tanımlayıcı duyusal analiz yöntemi ile eğitimli panelistler tarafından karakterize edilmiştir. Peynirlerin genel bileşiminin farklı olduğu ve kurumadde ve yağ içeriğinin sırasıyla % 54,27-64,66 ve % 23,11-28,96 arasında değiştiği saptanmıştır. Peynirlerin renk ölçümleri ve toplam serbest aminoasit, hidrolitik ransidite ve olgunlaşma düzeyleri arasında da farklılıklar olduğu görülmüştür. Tekstürel özellikler bakımından peynir örneklerinde sertlik, iç yapışkanlık, dış yapışkanlık, esneme, elastikiyet, çiğnenebilirlik ve sakızımsılık parametreleri belirlenmiştir. Özellikle, peynirlerin sertlik, dış yapışkanlık, çiğnenebilirlik ve sakızımsılık ölçümleri arasında geniş varyasyon olduğu saptanmıştır. Peynirlerin karakteristik duyusal terimleri pişmiş, peyniraltı suyu, kremamsı, sülfür, ransit, fermente, tatlı, tuzlu ve umamidir. Bazı peynirlerde maya-küf aroması ve acı tat algılanmıştır. Uçucu bileşen kompozisyonunun belirlenmesi için altı peynir örneği kullanılmıştır. Aldehitler, ketonlar, alkoller, asitler ve esterler analiz edilen peynirlerde belirlenen yaygın uçucu bileşenlerdir. Peynirlerde pinen ve limonen gibi terpenler de bulunmuştur. Asetik, bütanoik, hekzanoik, oktanoik ve dekanoik asitler E3 hariç diğer peynirlerde başlıca uçucu bileşenlerdir. Anahtar sözcükler: Ezine Eski Kaşar Peyniri, Fiziksel ve Kimyasal Özellikler, Uçucu Bileşen, Duyusal.Öğe Machine Learning-Assisted Near- and Mid-Infrared spectroscopy for rapid discrimination of wild and farmed Mediterranean mussels (Mytilus galloprovincialis)(Elsevier Inc., 2024) Ayvaz, Hüseyin; Temizkan, Rıza; Kaya, Burcu; Salman, Merve; Menevşeoğlu, Ahmed; Ayvaz, Zayde; Güneş, Nurhan; Doğan, Muhammed Ali; Mortaş, MustafaThe objective of this study was to investigate the ability to discriminate between wild and farmed Mediterranean mussels (Mytilus galloprovincialis) using machine learning-assisted near-infrared (NIR) and mid-infrared (MIR) spectroscopy. Mussels are of significant global importance in aquaculture due to their nutritional characteristics, encompassing a rich source of protein, essential fatty acids, various vitamins, and abundant minerals. Additionally, their ease of farming adds to their value as a desirable aquaculture species. The mussels' capacity to reflect environmental quality attributes makes them valuable as biomonitoring agents. However, differences in nutritional composition may arise between wild mussels harvested from natural marine hard-bottoms and those farmed in open artificial systems in the sea. In this study aimed at distinguishing between the two types of mussels, the classification models were created, and the most accurate results were achieved using the FT-MIR spectral data extracted from the interior part of the mussels, while the performance of FT-MIR data obtained from the mussels' shells was slightly lower, with the accuracy of 92% and R2 of 0.87. Still, the accuracies of all the classification models were over 90%. The Ensemble model, trained using FT-MIR spectra from the interior part of the mussel, achieved an accuracy of 98.4%, surpassing the performance of other variable sets. In both NIR and MIR models, spectra from the mussels' interior provide better discrimination than spectra from the outer shell.Öğe Machine learning-assisted near-infrared spectroscopy for rapid discrimination of apricot kernels in ground almond(Elsevier Ltd, 2024) Menevşeoglu, A.; Entrenas, J. A.; Güneş, N.; Doğan, Muhammed Ali; Perez-Marin, D.Almonds are one of the most widely consumed seeds in the world, both for their taste and for their high nutritional value. A rapid and non-destructive method to detect adulteration of ground almond with apricot kernels is a necessity in the food industry because of almond's high commodity value and being one of the most consumed tree nuts. Almonds are a target for economically motivated adulteration, and apricot kernel is the most seen adulterant in ground almond. NIR spectroscopy is simple, non-destructive, and cheaper alternatives to traditional methods including chromatography for the detection of almond adulteration. A total of 120 almond samples were purchased in Türkiye. NIR spectra were collected using a portable and benchtop spectrometer and analyzed by Soft Independent Modeling of Class Analogy (SIMCA) and Conditional Entropy (CE) with machine learning algorithms to generate a classification model to authenticate ground almonds. Partial Least Square Regression (PLSR) and CE with machine learning algorithms were used to predict the levels of apricot kernel in ground almonds. Ground almonds were adulterated with apricot kernels at different level (0–50%) with 2% intervals. Both SIMCA and CE algorithms combined with spectral data obtained from the spectrometers provided very distinct clusters for pure and adulterated samples (100% accuracy). Both units also showed superior performance in predicting apricot kernels using PLSR with rval>0.96 with a standard error prediction (SEP) 3.98%. Besides, CE with machine learning algorithms reveal similar performance using benchtop NIR spectrometer (SEP>4.49). Based on the SIMCA, PLSR, and CE-based models, NIR spectroscopy can be used as an alternative methods and showed great potential for real-time surveillance to detect apricot kernel adulteration in ground almond.Öğe Near- and mid-infrared determination of some quality parameters of cheese manufactured from the mixture of different milk species(Springer, 2021) Ayvaz, Hüseyin; Mortas, Mustafa; Doğan, Muhammed Ali; Atan, Mustafa; Yıldız Tiryaki, Gülgün; Karagül Yüceer, YoncaThis study aimed to evaluate the performance of both near-infrared (NIR) diffuse reflectance and mid-infrared-attenuated total reflectance (MIR-ATR) in determining some quality parameters of a commercial white cheese made of unknown ratios of various milk species. For this purpose, 81 commercial Ezine cheese samples, a special ripened cheese produced in Turkey, containing unknown ratios of bovine, caprine, and ovine milk, were used. Reference analyses, including textural properties, protein content, nitrogen fractions, ripening index coefficients, fat, salt, dry matter-moisture, and ash contents as well as pH and titratable acidity levels, were conducted in the samples following the traditional gold standards. For NIR applications, the spectra of both intact cubes and hand-crushed cheese samples were collected, whereas the spectra of only hand-crushed cheese samples were collected for MIR-ATR. PLSR (Partial Least Squares Regression) calibration models were developed for each parameter (n = 61) and then validated using both cross-validation (leave-one-out approach) and an external validation set (n = 20). Overall, PLSR models developed for total protein, fat, salt, dry matter, moisture, and ash content, as well as pH and titratable acidity, yielded satisfactory performance statistics in the complementary use of NIR and MIR spectroscopy. However, PLSR models of the other parameters, including textural properties, nitrogen fractions, and the ripening index, could only separate high and low values and were not able to make accurate quantitative predictions. NIR spectroscopy was found to be more accurate than that of MIR-ATR spectroscopy for almost all the parameters except for pH and titratable acidity, for which MIR-ATR spectroscopy was superior.Öğe Rapid detection of green pea adulteration in ground pistachio nuts using near and mid-infrared spectroscopy(Volkan OKATAN, 2020) Temizkan, Riza; Doğan, Muhammed Ali; Atakan, Orhan; Nazlım, Burak Alptuğ; Ayvaz, HüseyinNear-infrared (NIR) diffuse reflectance and Mid-infrared-attenuated total reflectance (MIR-ATR) spectroscopy were evaluated to determine the dried green pea seed adulteration in ground pistachio nuts. Sixty-three samples (51 for calibration and 12 for external validation sets) of ground pistachio nuts were deliberately adulterated with varying levels of dried green pea seeds (ranging from 0 to 50% (w/w)). Subsequently, both NIR and MIR-ATR spectra of the samples were collected separately. The quantitative predictions of green pea ratio in the samples were achieved using PLSR (Partial Least Squares Regression). Based on the PLSR models, SEP (Standard error of prediction) values of the models were 2.55 and 9.14% for NIR and MIR-ATR, respectively. The rPred (Correlation coefficient of prediction) values of the models were 0.99 and 0.80 for NIR and MIR-ATR, respectively. Additionally, the nondimensional values of RPD (Residual Predictive Deviation) for NIR and MIR-ATR spectra were calculated as 5.7 and 1.6, respectively. These results showed that the NIR-based models provided a distinct advantage over MIR-ATR-based models in accurately estimating the ratio of the dried green pea seeds in binary mixtures. Therefore, NIR spectroscopy has the potential and could be implemented in the routine applications of green pea detection in ground pistachio nuts.Öğe TENEKE TULUM PEYNİRLERİNİN BAZI FİZİKOKİMYASAL VE DUYUSAL ÖZELLİKLERİNİN BELİRLENMESİ(2023) Şen, Cengiz; Doğan, Muhammed Ali; Karagül Yüceer, YoncaÇalışmada, İzmir ve Çanakkale’de bulunan üreticilerden sağlanan İzmir Teneke Tulum peynirlerinin bazı karakteristik özelliklerinin belirlenmesi hedeflenmiştir. Peynirlerin kurumaddesinin ve yağ içeriğinin sırasıyla %48.59-61.18 ve %20.72-30.46 arasında değiştiği belirlenmiştir. Peynirlerde olgunlaşma süresi uzadıkça kurumadde, yağ, titrasyon asitliği ve hidrolitik ransidite değerlerinin arttığı ortaya konmuştur. Peynirlerin L* değerleri 78.41-88.80, sertlik değerleri ise 430-5213.75 arasında değişmektedir. Tanımlayıcı duyusal analizler sonucunda ‘pişmiş’, ‘peyniraltı suyu’, ‘kremamsı’, ‘sülfür’, ‘ransit’, ‘tuzlu’, ‘ekşi’, ‘umami’ ve ‘keskin’ terimleri panelistlerce geliştirilmiş bazı terimlerdir. En yoğun hissedilen aroma ve tat terimleri; pişmiş, peyniraltı suyu, kremamsı ve tuzludur. Duyusal analiz sonuçlarına göre seçilen altı peynirde uçucu bileşen analizi gerçekleştirilmiş olup asit ve ester grubu bileşenlerin yüksek konsantrasyonlarda olduğu saptanmıştır.
