You will be shown an abstract from a biomedical research paper. Given this abstract, your task is to extract all unique entities of the following types: ["Chemical"].

Please return the output as a JSON object of the format: {"Chemical": ["Arsenic", ...]}. The keys should be entity types and values should be lists of extracted entities belonging to the corresponding type. If you cannot find entities belonging to a specific type, the value should be [].

Only output the JSON object and do not include any additional text.

Abstract:

Identification of active compounds in vegetal extracts based on correlation between activity and HPLC-MS data. We propose a method identifying candidates for active compounds in vegetal extracts. From a collection of samples, the method requires, for each sample, a HPLC-MS analysis and a measurement of the activity. By applying a correlation analysis between the activity and the chromatographic area for each interval of elution time and m/z ratio, the peaks corresponding to candidates for active compounds can be identified. Additionally, when peaks are identified, a model can be estimated to predict the activity in new samples. Both methods are evaluated in one experiment involving the phenolic extract (PE) from 22 samples of extra virgin olive oil (EVOO) where the activity is a cytotoxicity index against JIMT-1 breast cancer cells. In this experiment, the samples were separated into two disjunct partitions: one was used for training (identification of candidates and estimation of prediction model), while the other was used for validation (by comparing the predicted and the measured activities). Three compounds were identified as candidates to be responsible for the cytotoxicity of the EVOO-PE against JIMT-1 cells. The prediction model provided an accurate estimation of the activity.
{"Chemical": []}