Introduction to Descriptive Analysis

Kemp, Sarah E.; Ng, M.; Hollowood, Tracey; Hort, Joanne

doi:10.1002/9781118991657.ch1

Cited by 27 publications

(20 citation statements)

References 115 publications

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“…The number of samples (N = 9) was adequate and enough for sensory evaluation to avoid fatigue of the panelists due to the alcohol concentration and astringency present in the wine, this makes the results more reliable. This is in accordance with the recommended maximum number of samples, which is usually between six and twelve for descriptive sensory evaluation [57,58].…”

Section: Descriptive Sensory Evaluationsupporting

confidence: 83%

Machine Learning Modeling of Wine Sensory Profiles and Color of Vertical Vintages of Pinot Noir Based on Chemical Fingerprinting, Weather and Management Data

Fuentes

Torrico

Tongson

et al. 2020

Sensors

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Important wine quality traits such as sensory profile and color are the product of complex interactions between the soil, grapevine, the environment, management, and winemaking practices. Artificial intelligence (AI) and specifically machine learning (ML) could offer powerful tools to assess these complex interactions and their patterns through seasons to predict quality traits to winegrowers close to harvest and before winemaking. This study considered nine vintages (2008–2016) using near-infrared spectroscopy (NIR) of wines and corresponding weather and management information as inputs for artificial neural network (ANN) modeling of sensory profiles (Models 1 and 2 respectively). Furthermore, weather and management data were used as inputs to predict the color of wines (Model 3). Results showed high accuracy in the prediction of sensory profiles of vertical wine vintages using NIR (Model 1; R = 0.92; slope = 0.85), while better models were obtained using weather/management data for the prediction of sensory profiles (Model 2; R = 0.98; slope = 0.93) and wine color (Model 3; R = 0.99; slope = 0.98). For all models, there was no indication of overfitting as per ANN specific tests. These models may be used as powerful tools to winegrowers and winemakers close to harvest and before the winemaking process to maintain a determined wine style with high quality and acceptability by consumers.

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Section: Descriptive Sensory Evaluationsupporting

confidence: 83%

Machine Learning Modeling of Wine Sensory Profiles and Color of Vertical Vintages of Pinot Noir Based on Chemical Fingerprinting, Weather and Management Data

Fuentes

Torrico

Tongson

et al. 2020

Sensors

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“…Metric distances among the chromatic coordinates data were processed by one-way ANOVA, followed by the Tukey’s b post-hoc test. The reliability of the panel test assessments was analyzed by two-way completely randomized ANOVA with panellists and EO treatments as fixed factors [ 50 , 51 ] with the software Big Sensory Soft 2.0 (ver. 2018, Brescia, Italy), that is specifically developed for sensory analysis.…”

Section: Methodsmentioning

confidence: 99%

Essential Oils as Post-Harvest Crop Protectants against the Fruit Fly Drosophila suzukii: Bioactivity and Organoleptic Profile

Bedini

Cosci

Tani

et al. 2020

Insects

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The essential oils extracted from mandarin (Citrus reticulata Blanco) fruits, and from tea tree (Maleleuca alternifolia (Maiden and Betche) Cheel) leaves have been chemically analyzed and tested for their bioactivity against D. suzukii. Besides, to estimate consumers’ acceptability of the essential oil (EO) treatments, we evaluated their impact on the organoleptic characteristics of the EO-treated fruits. The main chemical constituents of the two EOs were 1,8-cineole and 4-terpineol for M. alternifolia (22.4% and 17.6% of the total components, respectively), and limonene (83.6% of the total components) for C. reticulata. The behavioral tests indicate that the two EOs are able to deter D. suzukii oviposition and that D. suzukii shows positive chemotaxis to low concentrations of the EOs and negative chemotaxis when the EO concentration increases. While no negative effects on the organoleptic profiles were detected for fruits treated with C. reticulata EO, the olfactory profile of fruits treated with M. alternifolia EO was so negative that they were defined as “not suitable for consumption” by panellists. Overall, our findings indicate that the use of EOs for the post-harvest protection of small fruits is feasible, provided that the essential oils are selected not only for their bioactivity against the insect pest but also for their affinity with the consumers’ sensorial system.

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“…However, the added citrus flavors were used to help mask the aroma, and the intensity ratings from Experiment 1 show no evidence that the Isolone sample had a higher perceived hop aroma. Additional methods, such as descriptive analysis [46,47] could be used to confirm the intensity ratings using a trained panel, versus the naïve consumers used here. Further, descriptive analysis methods might also determine whether there is evidence of cross-modal interactions between taste and aroma in the beer samples used here.…”

Section: Discussionmentioning

confidence: 99%

Discrimination of Isointense Bitter Stimuli in a Beer Model System

Higgins

Hayes

2020

Nutrients

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Prior work suggests humans can differentiate between bitter stimuli in water. Here, we describe three experiments that test whether beer consumers can discriminate between different bitterants in beer. In Experiment 1 (n = 51), stimuli were intensity matched; Experiments 2 and 3 were a difference from control (DFC)/check-all-that-apply (CATA) test (n = 62), and an affective test (n = 81). All used a commercial non-alcoholic beer spiked with Isolone (a hop extract), quinine sulfate dihydrate, and sucrose octaacetate (SOA). In Experiment 1, participants rated intensities on general labeled magnitude scales (gLMS), which were analyzed via ANOVA. In Experiment 2, participants rated how different samples were from a reference of Isolone on a 7-point DFC scale, and endorsed 13 attributes in a CATA task. DFC data were analyzed via ANOVA with Dunnett’s test to compare differences relative to a blind reference, and CATA data were analyzed via Cochran’s Q test. In Experiment 3, liking was assessed on labeled affective magnitude scales, and samples were also ranked. Liking was analyzed via ANOVA and rankings were analyzed with a Cochran–Mantel–Haenszel test. Experiment 1 confirmed that samples were isointense. In Experiment 2, despite being isointense, both quinine (p = 0.04) and SOA (p = 0.03) were different from Isolone, but no significant effects were found for CATA descriptors (all p values > 0.16). In Experiment 3, neither liking (p = 0.16) or ranking (p = 0.49) differed. Collectively, these data confirm that individuals can discriminate perceptually distinct bitter stimuli in beer, as shown previously in water, but these differences cannot be described semantically, and they do not seem to influence hedonic assessments.

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Introduction to Descriptive Analysis

Cited by 27 publications

References 115 publications

Machine Learning Modeling of Wine Sensory Profiles and Color of Vertical Vintages of Pinot Noir Based on Chemical Fingerprinting, Weather and Management Data

Machine Learning Modeling of Wine Sensory Profiles and Color of Vertical Vintages of Pinot Noir Based on Chemical Fingerprinting, Weather and Management Data

Essential Oils as Post-Harvest Crop Protectants against the Fruit Fly Drosophila suzukii: Bioactivity and Organoleptic Profile

Discrimination of Isointense Bitter Stimuli in a Beer Model System

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