Vinegar extraction from unripe shikuwasa (Citrus depressa L.), an Okinawan citrus fruit

Introduction. Nakamoto Seedless, a variety of shikuwasa (Citrus depressa L.) in Okinawa, can be used to produce vinegar extracts because it has no seeds causing bitter taste. However, Nakamono Seedless is hardly cultivated commercially in Okinawa. This research was aimed to develop vinegar extract from Ogimi Kugani, another major variety of shikuwasa, and compare its characteristics with those of extracts from Nakamoto Seedless. Study objects and methods. The study featured vinegar extracts from the whole shikuwasa of Nakamoto Seedless (20% fruit) and Ogimi Kunagi (5, 10, and 20% of fruit) varieties. The fruit was harvested in June, July, and August. We tested the samples for limonin and polymethoxyflavones content and sensory attributes, especially bitterness. Results and discussion. Vinegar extracts with 20% of Ogimi Kugani harvested in June and July tasted bitter compared to those from Nakamoto Seedless harvested in August, but extracts from Ogimi Kugani harvested in August were not bitter. In addition, 5 and 10% vinegar extracts from Ogimi Kugani harvested in June had lower bitterness. The vinegar extracts from both shikuwasa varieties contained polymethoxyflavones ‒ bioactive compounds ‒ and similar flavor. Conclusion. The whole shiluwasa fruit can be used to produce vinegar drinks, Ponzu soy sauce, salad dressings, etc.

Classification of Chinese vinegar varieties using electronic nose and fuzzy Foley-Sammon transformation

Due to the difference of raw materials and brewing technology, the quality and flavours of vinegar are different. Different kinds of vinegar have different functions and effects. Therefore, it is important to classify the vinegar varieties correctly. This work presented a new fuzzy feature extraction algorithm, called fuzzy Foley-Sammon transformation (FFST), and designed the electronic nose (E-nose) system for classifying vinegar varieties successfully. Principal component analysis (PCA) and standard normal variate (SNV) were used as the data preprocessing algorithms for the E-nose system. FFST, Foley-Sammon transformation (FST) and linear discriminant analysis (LDA) were used to extract discriminant information from E-nose data, respectively. Then, K nearest neighbor (KNN) served as a classifier for the classification of vinegar varieties. The highest identification accuracy rate was 96.92% by using the FFST and KNN. Therefore, the E-nose system combined with the FFST was an effective method to identify Chinese vinegar varieties and this method has wide application prospects.