Evaluating espresso coffee quality by means of time-series feature engineering

Espresso quality attracts the interest of many stakeholders: from consumers to local business activities, from coffee-machine vendors to international coffee industries. So far, it has been mostly addressed by means of human experts, electronic noses, and chemical approaches. The current work, instead, proposes a datadriven analysis exploiting time-series feature engineering.We analyze a real-world dataset of espresso brewing by professional coffee-making machines. The novelty of the proposed work is provided by the focus on the brewing time series, from which we propose to engineer features able to improve previous data-driven metrics determining the quality of the espresso. Thanks to the exploitation of the proposed features, better quality-evaluation predictions are achieved with respect to previous data-driven approaches that relied solely on metrics describing each brewing as a whole (e.g., average flow, total amount of water). Yet, the engineered features are simple to compute and add a very limited workload to the coffee-machine sensor-data collection device, hence being suitable for large-scale IoT installations on-board of professional coffee machines, such as those typically installed in consumer-oriented business activities, shops, and workplaces. To the best of the authors' knowledge, this is the first attempt to perform a data-driven analysis of real-world espresso-brewing time series. Presented results yield to three-fold improvements in classification accuracy of high-quality espresso coffees with respect to current data-driven approaches (from 30% to 100%), exploiting simple threshold-based quality evaluations, defined in the newly proposed feature space

A Comparative Study of Collaborative Filtering in Product Recommendation

Product recommendation is considered a well-known technique for bringing customers and products together. With applications in music, electronic shops, or almost any platform the user daily deals with, the recommendation system’s sole scope is to help customers and attract new ones to discover new products. Through product recommendation, transaction costs can also be decreased, improving overall decision-making and quality. To perform recommendations, a recommendation system must utilize customer feedback, such as habits, interests, prior transactions as well as information used in customer profiling, and finally deliver suggestions. Hence, data is the key factor in choosing the appropriate recommendation method and drawing specific suggestions. This research investigates the data challenges of recommendation systems, specifying collaborative-based, content-based, and hybrid-based recommendations. In this context, collaborative filtering is being explored, with the Surprise library and LightFM embeddings being analysed and compared on top of foodservice transactional data. The involved algorithms’ metrics are being identified and parameterized, while hyperparameters are being tuned properly on top of this transactional data, concluding that LightFM provides more efficient recommendation results following the evaluation’s precision and recall outcomes. Nevertheless, even though the Surprise library outperforms, it should be used when constructing user-friendly models, requiring low code and low technicalities. Doi: 10.28991/ESJ-2023-07-01-01 Full Text: PD

The Lumberjack, February 15, 1984

The student newspaper of Humboldt State University.https://digitalcommons.humboldt.edu/studentnewspaper/3078/thumbnail.jp

Brew Methods Effect on Coffee Flavor and Aroma

Coffee is one of the most popular aromatic hot drinks in the world. Numerous coffee brewing methods have been developed to make a cup of coffee. In industry, coffee flavor and aroma is determined by using a method called cupping, but in order to quantitate flavor and aroma in coffee, descriptive sensory is a better option. This study identified how brew methods influence coffee aroma and flavor. Four different roast level Folgers® commercial coffee (Breakfast blend, Classic roast, 100% Colombia and Black silk) were brewed by four brewing methods (pour over, drip, French press, and cold brew) and were tested using a trained descriptive panel using the World Coffee Research (WCR) coffee lexicon. Twenty-five main aroma attributes and thirty-five main flavor and texture attributes of coffee were used. Cold brew method produced the mildest coffee among the four brew methods while drip produced a much stronger coffee. The sensory aroma and flavor differences between different coffee types were not as great as differences between brew methods. From chemical tests, Brix percentage and TDS (Total Dissolved Solids) differed across coffee types and brew methods. Chemical attributes were closely associated with overall impact, body fullness, bitter basic taste and roasted and burnt flavor aromatics. Volatile compounds (n = 271) were identified. Forty-four volatile aromatic compounds differed across coffee types while thirty-seven volatile aromatic compounds differed across brew methods. Folgers® 100% Colombia coffee showed a difference from the other three coffee types by showing higher (P < 0.05) amount of volatile compounds, especially in 2-butenal, and 1-(2-hydroxyphenyl)-ethanone (beany aroma). Cold brewed Folgers® 100% Colombia was high on sweet, overall sweet flavor as well as 2,3-hexanedione. The preparation method is a critical factor affecting coffee flavor and aroma. Coffee from the cold brew method was more fruity, floral and sweet whereas coffee from the drip or French press methods were roasted, burnt, and ashy

Spartan Daily, February 28, 2001

Volume 116, Issue 24https://scholarworks.sjsu.edu/spartandaily/9660/thumbnail.jp

Spartan Daily, February 28, 2001

Volume 116, Issue 24https://scholarworks.sjsu.edu/spartandaily/9660/thumbnail.jp

Spartan Daily, February 28, 2001

Volume 116, Issue 24https://scholarworks.sjsu.edu/spartandaily/9660/thumbnail.jp