Electrical Impedance Spectroscopy-Based Detection of Internal Browning Disorder in Apples

Internal browning (IB)-related disorders in apples are causing significant economic losses, as they undermine consumer trust and market acceptability. Especially for susceptible cultivars, a comprehensive assessment of IB across the whole supply chain is crucial to meet consumer demand and trust, reduce food waste, and improve the profit margins of producers. To address these objectives, there is an urgent need for a fast, reliable, and portable nondestructive technique that enables real-time decisionmaking. In this study, apples were harvested early and late from two orchards and stored under two different conditions. After seven months storage, a representative sample of apples were analyzed using electrical impedance spectroscopy (EIS) to assess IB, categorizing the samples into healthy, slight brown, and severe brown. To validate the EIS results, a standard quality parameter, fruit firmness, was analyzed. The EIS spectrum shows that the magnitude in the lower frequency range (40 Hz to 1.4 kHz) and phase in mid-frequency range (1.4 to 15 kHz) yields the most promising results, with statistically significant differences (p<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\leq$</tex-math></inline-formula>0.001) and (p<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\leq$</tex-math></inline-formula>0.005), respectively. Contrarily, firmness measurement did not exhibit promising discrimination between healthy and internally browned apples (p-value of 0.21). Furthermore, the EIS spectrum of the three different classes were fitted using a single Cole equivalent model, revealing its efficacy as the best-fit equivalent circuit and offered valuable insights into the physio-chemical changes in biological cells. This work solidifies the EIS potential as a powerful tool for real-time, nondestructive, user-friendly, and cost-effective method in sustainable precision agriculture and food security assessment.