Standard Analytical Methods, Sensory Evaluation, NIRS and Electronic Tongue for Sensing Taste Attributes of Different Melon Varieties

Grafting by vegetables is a practice with many benefits, but also with some unknown influences on the chemical composition of the fruits. Our goal was to assess the effects of grafting and storage on the extracted juice of four orange-fleshed Cantaloupe type (Celestial, Donatello, Centro, Jannet) melons and two green-fleshed Galia types (Aikido, London), using sensory profile analysis and analytical instruments: An electronic tongue (E-tongue) and near-infrared spectroscopy (NIRS). Both instruments are known for rapid qualitative and quantitative food analysis. Linear discriminant analysis (LDA) was used to classify melons according to their varieties and storage conditions. Partial least square regression (PLSR) was used to predict sensory and standard analytical parameters. Celestial variety had the highest intensity for sensory attributes in Cantaloupe variety. Both green and orange-fleshed melons were discriminated and predicted in LDA with high accuracies (100%) using the E-tongue and NIRS. Galia and Cantaloupe inter-varietal classification with the E-tongue was 89.9% and 82.33%, respectively. NIRS inter-varietal classification was 100% with Celestial variety being the most discriminated as with the sensory results. Both instruments, classified different storage conditions of melons (grafted and self-rooted) with high accuracies. PLSR showed high accuracy for some standard analytical parameters, where significant differences were found comparing different varieties in ANOVA.

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Near-Infrared Spectroscopy for Discrimination of Charcoal from Eucalyptus and Native Cerrado Species—Contribution to a Database for Forestry Supervision

Forest Science ◽

10.1093/forsci/fxab015 ◽

2021 ◽

Author(s):

Silvana Nisgoski ◽

Thaís A P Gonçalves ◽

Júlia Sonsin-Oliveira ◽

Adriano W Ballarin ◽

Graciela I B Muñiz

Keyword(s):

Infrared Spectroscopy ◽

Discriminant Analysis ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

New Technologies ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Eucalyptus Species ◽

Linear Discriminant

Abstract The illegal charcoal trade is an internationally well-known forest crime. In Brazil, government agents try to control it using the document of forest origin (DOF). To confirm a load’s legality, the agents must compare it with the declared content of the DOF. However, to identify charcoal is difficult even for specialists in wood anatomy. Hence, new technologies would facilitate the agents’ work. Near-infrared spectroscopy (NIR) provides a rapid and precise response to differentiate carbonized species. Considering the rich Brazilian flora, NIR studies are still underdeveloped. Our work aimed to differentiate charcoals of seven eucalypts and 10 Cerrado species based on NIR analysis and to add information to a charcoal database. Data were collected with a spectrophotometer in reflectance mode. Partial least square regression with discriminant analysis (PLS-DA) and a linear discriminant analysis (LDA) was applied to confirm the performance and potential of NIR spectra to distinguish native Cerrado species from eucalyptus species. Wavenumbers from 4,000 to 6,000 cm−1 and transversal surface presented the best results. NIR had the potential to distinguish eucalypt charcoals from Cerrado species and in comparison to reference samples. NIR is a potential tool for forestry supervision to guarantee the sustainability of the charcoal supply in Brazil and countries with similar conditions. Study Implications It is a challenge to protect the Cerrado biome against deforestation for charcoal production. The application of new technologies such as near-infrared spectroscopy (NIR) for charcoal identification might improve the work of government agents. In this article, we studied the spectra of Cerrado and eucalypt species. Our results present good separation between the analyzed groups. The main goal is to develop a reliable NIR database that would be useful in the practical work of agents. The database will be available for all control agencies, and future training will be done for a rapid initial evaluation in the field.

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Identification and quantification of additives in bituminous binders based on FTIR spectroscopy and multivariate analysis methods

Materials and Structures ◽

10.1617/s11527-021-01763-1 ◽

2021 ◽

Vol 54 (4) ◽

Author(s):

Sandra Weigel ◽

Michael Gehrke ◽

Christoph Recknagel ◽

Dietmar A. Stephan

Keyword(s):

Ftir Spectroscopy ◽

Climatic Conditions ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Coefficient Of Determination ◽

Linear Discriminant ◽

Bituminous Binders ◽

Identification And Quantification

AbstractBitumen is a crucial building material in road construction, which is exposed to continuously higher stresses due to higher traffic loads and changing climatic conditions. Therefore, various additives are increasingly being added to the bitumen complicating the characterisation of the bituminous binder, especially concerning the reuse of reclaimed asphalt. Therefore, this work aimed to demonstrate that the combination of Fourier transform infrared (FTIR) spectroscopy with attenuated total reflexion (ATR) technique and multivariate evaluation is a very well-suited method to reliable identify and quantify additives in bituminous binders. For this purpose, various unmodified and modified binders, directly and extracted from laboratory and reclaimed asphalts, were investigated with FTIR-ATR spectroscopy. The determined spectra, pre-processed by standard normal variate (SNV) transformation and the determination of the 1st derivation, were evaluated using factor analysis (FA), linear discriminant analysis (LDA) and partial least square regression (PLSR). With this multivariate evaluation, first, a significant model with a very high hit rate of over 90% was developed allowing for the identification of styrene-butadiene copolymers (SBC), ethylene-copolymer bitumen (ECB) and different waxes (e.g. amide and Fischer-Tropsch wax) even if the additives do not show any additional peaks or the samples are multi-modified. Second, a quantification of the content is possible for SBC, ECB, and amide wax with a mean error of RMSE ≤ 0.4 wt% and a coefficient of determination of R2 > 90%. Based on these results, FTIR identification and quantification of additives in bituminous binders is a very promising method with a great potential.

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A Note on the Tracing of Herbage Contribution to Grazing Sheep Diet Using Milk and Feces Biomarkers

Frontiers in Veterinary Science ◽

10.3389/fvets.2021.623784 ◽

2021 ◽

Vol 8 ◽

Author(s):

Giovanni Molle ◽

Andrea Cabiddu ◽

Mauro Decandia ◽

Marco Acciaro ◽

Giuseppe Scanu ◽

...

Keyword(s):

Dry Matter ◽

Mean Value ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Milk Fatty Acids ◽

Linear Discriminant ◽

Animal Data ◽

The Mean ◽

Explained Variance

Milk from grazing ruminants is usually rich in beneficial components for human health, but distinguishing milks sourced from grazing is difficult, and this hinders the valuing of the grazing benefit. This study aimed at evaluating the ability of milk biomarkers (1) to trace milks sourced from sheep submitted to different access times (ATs) to pasture and (2) to estimate sheep herbage dry matter intake (HDMI, g DM ewe−1 d−1) and herbage percentage (HP, % DM) in sheep diet. Animal data derive from a published experiment in which six replicated groups of mid-lactation Sarda sheep had ATs of 2, 4, or 6 h d−1 to a ryegrass pasture. Sheep HDMI and HP of each group were measured on four dates in April 2013. Group milk was sampled, and milk fatty acids (FAs) and n-alkanes were determined by gas chromatography. The latter markers were also measured in feces samples bulked by group. The data (N = 24 records) were submitted to Linear Discriminant Analysis (LDA) aimed at distinguishing the AT to pasture based on biomarkers previously selected by Genetic Algorithms (GA). Partial Least Square Regression (PLSR) models were used to estimate HDMI and HP using biomarkers selected by GA. Based on one milk alkane and six milk FAs as biomarkers, estimates of the AT using GA-LDA were 95.8% accurate. The estimation of HDMI by GA-PLSR based on five milk FAs was moderately precise [explained variance = 75.2%; percentage of the residual mean square error of cross-validation over the mean value (RMSECV%) = 15.0%]. The estimation of HP by GA-PLSR based on 1 milk alkane and 10 FAs was precise (explained variance = 80.8%; RMSECV% = 7.4%). To conclude, these preliminary results suggest that milks sourced from sheep flocks with AT to pasture differentiated by 2 h in the range 2–6 h d−1 can be precisely discriminated using milk biomarkers. The contribution of herbage to sheep diet can also be precisely estimated.

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Prediction of Physicochemical Properties in Honeys with Portable Near-Infrared (microNIR) Spectroscopy Combined with Multivariate Data Processing

Foods ◽

10.3390/foods10020317 ◽

2021 ◽

Vol 10 (2) ◽

pp. 317

Author(s):

Olga Escuredo ◽

María Shantal Rodríguez-Flores ◽

Laura Meno ◽

María Carmen Seijo

Keyword(s):

Physicochemical Properties ◽

Near Infrared ◽

Prediction Models ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Botanical Origin ◽

Linear Discriminant ◽

Physicochemical Composition ◽

Natural Foods

There is an increase in the consumption of natural foods with healthy benefits such as honey. The physicochemical composition contributes to the particularities of honey that differ depending on the botanical origin. Botanical and geographical declaration protects consumers from possible fraud and ensures the quality of the product. The objective of this study was to develop prediction models using a portable near-Infrared (MicroNIR) Spectroscopy to contribute to authenticate honeys from Northwest Spain. Based on reference physicochemical analyses of honey, prediction equations using principal components analysis and partial least square regression were developed. Statistical descriptors were good for moisture, hydroxymethylfurfural (HMF), color (Pfund, L and b* coordinates of CIELab) and flavonoids (RSQ > 0.75; RPD > 2.0), and acceptable for electrical conductivity (EC), pH and phenols (RSQ > 0.61; RDP > 1.5). Linear discriminant analysis correctly classified the 88.1% of honeys based on physicochemical parameters and botanical origin (heather, chestnut, eucalyptus, blackberry, honeydew, multifloral). Estimation of quality and physicochemical properties of honey with NIR-spectra data and chemometrics proves to be a powerful tool to fulfil quality goals of this bee product. Results supported that the portable spectroscopy devices provided an effective tool for the apicultural sector to rapid in-situ classification and authentication of honey.

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التحليل المميز والانحدار اللوجستي بأستعمال المربعات الصغرى الجزئية (دراسة تجريبية (محاكاة))

Journal of Economics and Administrative Sciences ◽

10.33095/jeas.v24i106.32 ◽

2018 ◽

Vol 24 (106) ◽

pp. 407

Author(s):

رباب عبد الرضا صالح ◽

محمد شاكر محمود

Keyword(s):

Logistic Regression ◽

Discriminant Function ◽

Binary Logistic Regression ◽

Partial Least Square ◽

Linear Discriminant Function ◽

Least Square ◽

Partial Least Square Regression ◽

Linear Discriminant ◽

Least Square Regression

يعد اسلوبي الانحدار اللوجستي الثنائي Binary logistic regression والدالة المميزة الخطية Linear discriminant function من اهم الاساليب الاحصائية المستخدمة في التصنيف والتنبؤ، عندما تكون البيانات من النوع الثنائي (0،1) فانه لا يمكن استخدام الانحدار الاعتيادي فلذلك نلجأ الى الانحدار اللوجستي الثنائي والدالة المميزة الخطية في حالة وجود مجموعتين او اكثر، وفي حالة وجود مشكلة التعدد الخطي Multicollinearity بين البيانات (ان البيانات يوجد فيها ارتباطات عالية بين المتغيرات) اصبح عدم الامكان في استخدام الانحدار اللوجستي والدالة المميزة الخطية، ولحل هذه المشكلة توجد عدة طرائق منها طريقة انحدار المربعات الصغرى الجزئية Partial least square regression لحل مشكلة التعدد الخطي. وقد جرى في هذه البحث المقارنة بين الانحدار اللوجستي الثنائي binary logistic regression والدالة المميزة الخطية linear discriminant function عن طريق خطأ التصنيف. حيث تم توليد بيانات بمتغير استجابة (Y) نوع ثنائي (0,1) تحتوي على مشكلة التعدد الخطي وبحجوم عينات (50-100-150-250-400) ومتغيرات (5-10-20). حيث تمت معالجة مشكلة التعدد الخطي بأستعمال طريقة المربعات الصغرى الجزئية Partial least square. وتوصل البحث الى ان الدالة المميزة الخطية linear discriminant function هي أفضل في تصنيف البيانات من الانحدار اللوجستي الثنائي binary logistic regression، اذ صنفت الدالة المميزة البيانات بشكل صحيح وأكثر دقة من الانحدار اللوجستي الثنائي.

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Sensory Drivers of Consumer Acceptance, Purchase Intent and Emotions toward Brewed Black Coffee

Foods ◽

10.3390/foods11020180 ◽

2022 ◽

Vol 11 (2) ◽

pp. 180

Author(s):

Ammaraporn Pinsuwan ◽

Suntaree Suwonsichon ◽

Penkwan Chompreeda ◽

Witoon Prinyawiwatkul

Keyword(s):

Positive Emotions ◽

Bitter Taste ◽

Partial Least Square ◽

Least Square ◽

Consumer Acceptance ◽

Sensory Characteristics ◽

Partial Least Square Regression ◽

Sensory Profile ◽

Purchase Intent ◽

Wide Range

The link between coffee aroma/flavor and elicited emotions remains underexplored. This research identified key sensory characteristics of brewed black coffee that affected acceptance, purchase intent and emotions for Thai consumers. Eight Arabica coffee samples were evaluated by eight trained descriptive panelists for intensities of 26 sensory attributes and by 100 brewed black coffee users for acceptance, purchase intent and emotions. Results showed that the samples exhibited a wide range of sensory characteristics, and large differences were mainly described by the attributes coffee identity (coffee ID), roasted, bitter taste, balance/blended and fullness. Differences also existed among the samples for overall liking, purchase intent and most emotion terms. Partial least square regression analysis revealed that liking, purchase intent and positive emotions, such as active, alert, awake, energetic, enthusiastic, feel good, happy, jump start, impressed, pleased, refreshed and vigorous were driven by coffee ID, roasted, ashy, pipe tobacco, bitter taste, rubber, overall sweet, balanced/blended, fullness and longevity. Contrarily, sour aromatic, sour taste, fruity, woody, musty/earthy, musty/dusty and molasses decreased liking, purchase intent and positive emotions, and stimulated negative emotions, such as disappointed, grouchy and unfulfilled. This information could be useful for creating or modifying the sensory profile of brewed black coffee to increase consumer acceptance.

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Rapid and Nondestructive Determination of Egg Freshness Category and Marked Date of Lay using Spectral Fingerprint

Journal of Spectroscopy ◽

10.1155/2020/8838542 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11

Author(s):

Thomas O. S. Akowuah ◽

Ernest Teye ◽

Julius Hagan ◽

Kwasi Nyandey

Keyword(s):

Cold Storage ◽

Principal Components ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Linear Discriminant ◽

Identification Rate ◽

Least Square Regression ◽

Ambient Storage ◽

Egg Freshness

The potential of nondestructive prediction of egg freshness based on near-infrared (NIR) spectra fingerprints would be beneficial to quality control officers and consumers alike. In this study, handheld NIR spectrometer in the range of 740 nm to 1070 nm and chemometrics were used to simultaneously determine egg freshness based on marked date of lay for eggs stored under cold and ambient conditions. The spectra acquired from the eggs were preprocessed using multiplicative scatter correction and principal component analysis (MSC-PCA). Linear discriminant analysis (LDA) was used to build identification model to predict the category of freshness, while partial least square regression (PLS-R) was used to determine the marked date of lay. The performance of LDA model was above 95% identification rate in both calibration and prediction set for the eggs stored under ambient and cold storage. For eggs stored in ambient storage, LDA had 95.54% identification rate at 5 principal components, while at cold storage LDA has 100% identification rate at 5 principal components for determining the marked date of lay, and partial least square regression (PLS-R) gave R = 0.87 and RMSEI = 2.57 for ambient storage and R = 0.88 and RMSEI = 2.66 for cold storage in independent set, respectively. The results show that handheld spectrometer and multivariate analysis could be used for rapid and nondestructive measurement of egg freshness. This provides a novel solution for egg integrity prediction along the value chain.

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Correlating Noble Rot Infection of Garganega Withered Grapes with Key Molecules and Odorants of Botrytized Passito Wine

Foods ◽

10.3390/foods8120642 ◽

2019 ◽

Vol 8 (12) ◽

pp. 642 ◽

Cited By ~ 2

Author(s):

Barbara Simonato ◽

Marilinda Lorenzini ◽

Michela Cipriani ◽

Fabio Finato ◽

Giacomo Zapparoli

Keyword(s):

Seasonal Variation ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Sensory Profile ◽

Positive Contribution ◽

Predictive Tool ◽

Aroma Profile ◽

Least Square Regression ◽

Oxidative Effects

Experimental passito wines with different percentages of naturally noble-rotten grapes of the Garganega variety were analyzed to evaluate key molecules and odorants related to the typical aroma and sensory profile of botrytized passito wine. Remarkable changes in the concentration of 1-octen-3-ol, 4-terpineol, benzaldehyde, N-(3-methylbutyl)acetamide, and sherry lactone 1 and 2 were observed between sound and noble-rotten wines. Wines were perceived to be different for floral, honey, figs, apricot, and caramel scents. By partial least square regression these descriptors were well correlated to samples. An important positive contribution of sherry lactones, N-(3-methylbutyl)acetamide, vanillin, benzaldehyde, and γ-butyrolactone to honey, apricot, and caramel was observed. It is conceivable that oxidative effects of Botrytis cinerea infection play an important role in the genesis of these chemical and sensory aroma markers. This study provides a predictive tool for winemakers that use natural grape withering to produce wines whose aroma profile is not standardized due to the seasonal variation of noble rot incidence.

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التحليل المميز والانحدار اللوجستي بوجود مشكلة التعدد الخطي (دراسة تطبيقية على مرض فقر الدم)

Journal of Economics and Administrative Sciences ◽

10.33095/jeas.v23i99.261 ◽

2017 ◽

Vol 23 (99) ◽

pp. 373

Author(s):

رباب عبد الرضا صالح البكري ◽

محمد شاكر محمود

Keyword(s):

Logistic Regression ◽

Discriminant Function ◽

Binary Logistic Regression ◽

Partial Least Square ◽

Linear Discriminant Function ◽

Least Square ◽

Partial Least Square Regression ◽

Linear Discriminant ◽

Least Square Regression

المستخلص تعد طريقة الانحدار اللوجستي الثنائي Binary logistic regression والدالة المميزة الخطية Linear discriminant function من اهم الطرائق الاحصائية المستخدمة في التصنيف والتنبؤ، عندما تكون البيانات من النوع الثنائي (0،1) فانه لا يمكن استخدام الانحدار الاعتيادي فلذلك نلجأ الى الانحدار اللوجستي الثنائي والدالة المميزة الخطية في حالة وجود مجموعتين، وفي حالة وجود مشكلة التعدد الخطي Multicollinearity بين البيانات (ان البيانات يوجد فيها ارتباطات عالية بين المتغيرات) اصبح عدم الامكان في استخدام الانحدار اللوجستي والدالة المميزة الخطية، ولحل هذه المشكلة نلجأ الى طريقة انحدار المربعات الصغرى الجزئية Partial least square regression لحل مشكلة التعدد الخطي. وقد جرى في هذه البحث المقارنة بين الانحدار اللوجستي الثنائي binary logistic regression والدالة المميزة الخطية linear discriminant function عن طريق خطأ التصنيف. حيث تم جمع بيانات عن مرض فقر الدم بمتغيرين هما فقر الدم الحاد بالرمز (0)، وفقر الدم المزمن بالرمز (1) وبعدة متغيرات حول المرض. جمعت البيانات من عدة مستشفيات عراقية، وجمعت عينة من المرضى الراقدين في المستشفى وحالات سابقة رقدت في المستشفى بعينة قدرها (140) مريضاً مصاباً بهذا المرض. وعند اختبار البيانات وجدت ان هناك مشكلة التعدد الخطي Multicollinearity تمت معالجتها بأستعمال طريقة المربعات الصغرى الجزئية Partial least square. وتوصل البحث الى ان الدالة المميزة الخطية linear discriminant function هي أفضل في تصنيف البيانات من الانحدار اللوجستي الثنائي binary logistic regression، اذ صنفت الدالة المميزة البيانات بشكل صحيح وأكثر دقة من الانحدار اللوجستي الثنائي.

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Taste Masking Study Based on an Electronic Tongue: the Formulation Design of 3D Printed Levetiracetam Instant-Dissolving Tablets

Pharmaceutical Research ◽

10.1007/s11095-021-03041-9 ◽

2021 ◽

Author(s):

Zengming Wang ◽

Jingru Li ◽

Xiaoxuan Hong ◽

Xiaolu Han ◽

Boshi Liu ◽

...

Keyword(s):

Drug Release ◽

Electronic Tongue ◽

Principal Component ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Taste Masking ◽

Formulation Design ◽

3D Printed

Abstract Purpose Proper taste-masking formulation design is a critical issue for instant-dissolving tablets (IDTs). The purpose of this study is to use the electronic tongue to design the additives of the 3D printed IDTs to improve palatability. Methods A binder jet 3D printer was used to prepare IDTs of levetiracetam. A texture analyzer and dissolution apparatus were used to predict the oral dispersion time and in vitro drug release of IDTs, respectively. The palatability of different formulations was investigated using the ASTREE electronic tongue in combination with the design of experiment and a model for masking bitter taste. Human gustatory sensation tests were conducted to further evaluate the credibility of the results. Results The 3D printed tablets exhibited rapid dispersion (<30 s) and drug release (2.5 min > 90%). The electronic tongue had an excellent ability of taste discrimination, and levetiracetam had a good linear sensing performance based on a partial least square regression analysis. The principal component analysis was used to analyze the signal intensities of different formulations and showed that 2% sucralose and 0.5% spearmint flavoring masked the bitterness well and resembled the taste of corresponding placebo. The results of human gustatory sensation test were consistent with the trend of the electronic tongue evaluation. Conclusions Owing to its objectivity and reproducibility, this technique is suitable for the design and evaluation of palatability in 3D printed IDT development.

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