Empirical Evaluation of the Impact of Refactoring on Internal Quality Attributes

Author(s):  
Muh. Riansyah ◽  
Petrus Mursanto
HortScience ◽  
1998 ◽  
Vol 33 (3) ◽  
pp. 544c-544
Author(s):  
A. Hakim ◽  
A. Purvis ◽  
E. Pehu ◽  
I. Voipio ◽  
E. Kaukovirta

Both external and internal quality of fruits such as tomatoes can be evaluated by different methods, but all most all of the methods are destructive. For this reason, there is a need to reassess some of the alternative techniques. Nondestructive quality evaluation is an attractive alternative. The principles of different nondestructive quality evaluation techniques such as optical, physical, and fluorescence techniques applied to tomato fruit is explained. Successful application of these techniques that could be used for evaluation of different quality attributes are illustrated. The advantages of nondestructive quality evaluation techniques are that they are very fast, easy, labor- and time-intensive, and inexpensive. These techniques could also be useful to evaluate the quality of other vegetables.


2021 ◽  
Vol 11 (7) ◽  
pp. 3209
Author(s):  
Karla R. Borba ◽  
Didem P. Aykas ◽  
Maria I. Milani ◽  
Luiz A. Colnago ◽  
Marcos D. Ferreira ◽  
...  

Portable spectrometers are promising tools that can be an alternative way, for various purposes, of analyzing food quality, such as monitoring in a few seconds the internal quality during fruit ripening in the field. A portable/handheld (palm-sized) near-infrared (NIR) spectrometer (Neospectra, Si-ware) with spectral range of 1295–2611 nm, equipped with a micro-electro-mechanical system (MEMs), was used to develop prediction models to evaluate tomato quality attributes non-destructively. Soluble solid content (SSC), fructose, glucose, titratable acidity (TA), ascorbic, and citric acid contents of different types of fresh tomatoes were analyzed with standard methods, and those values were correlated to spectral data by partial least squares regression (PLSR). Fresh tomato samples were obtained in 2018 and 2019 crops in commercial production, and four fruit types were evaluated: Roma, round, grape, and cherry tomatoes. The large variation in tomato types and having the fruits from distinct years resulted in a wide range in quality parameters enabling robust PLSR models. Results showed accurate prediction and good correlation (Rpred) for SSC = 0.87, glucose = 0.83, fructose = 0.87, ascorbic acid = 0.81, and citric acid = 0.86. Our results support the assertion that a handheld NIR spectrometer has a high potential to simultaneously determine several quality attributes of different types of tomatoes in a practical and fast way.


2021 ◽  
Vol 11 (2) ◽  
pp. 796
Author(s):  
Alhanoof Althnian ◽  
Duaa AlSaeed ◽  
Heyam Al-Baity ◽  
Amani Samha ◽  
Alanoud Bin Dris ◽  
...  

Dataset size is considered a major concern in the medical domain, where lack of data is a common occurrence. This study aims to investigate the impact of dataset size on the overall performance of supervised classification models. We examined the performance of six widely-used models in the medical field, including support vector machine (SVM), neural networks (NN), C4.5 decision tree (DT), random forest (RF), adaboost (AB), and naïve Bayes (NB) on eighteen small medical UCI datasets. We further implemented three dataset size reduction scenarios on two large datasets and analyze the performance of the models when trained on each resulting dataset with respect to accuracy, precision, recall, f-score, specificity, and area under the ROC curve (AUC). Our results indicated that the overall performance of classifiers depend on how much a dataset represents the original distribution rather than its size. Moreover, we found that the most robust model for limited medical data is AB and NB, followed by SVM, and then RF and NN, while the least robust model is DT. Furthermore, an interesting observation is that a robust machine learning model to limited dataset does not necessary imply that it provides the best performance compared to other models.


2021 ◽  
Vol 332 ◽  
pp. 01002
Author(s):  
Elinda Okstaviyani ◽  
Kawiji ◽  
Raden Baskara Katri Anandhito ◽  
Asri Nursiwi ◽  
Dimas Rahadian Aji Muhamnmad

Sappan wood (Caesalpinia sappan L.) is a spice that has a high polyphenol content and has the potential to enrich the chocolate taste. This study evaluated the panelists’ acceptance and physical analysis (color and hardness) of white and milk compound chocolate with the addition of Sappan wood powder (0 %, 5%, 10%, 15%) by implementing a completely randomized design (CRD) experiment with one factor. The results showed that the addition of Sappan wood powder could reduce the panelists’ preference at the parameters of color, aroma, taste, texture and overalls. Hence, Panelists preferred white and milk compound chocolate without the addition of Sappan wood powder. Results of the physical analysis showed that the addition of Sappan wood powder decreased the chocolate brightness and increased the chocolate hardness.


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