´ÓÎÂÊÒÉú²úµ½Ïû·ÑÕßÑ¡Ôñ£ºÐ¿ÉúÎïÇ¿»¯(zinc biofortification)ÓëÊÓ¾õÏßË÷(visual cues)µÄ×÷ÓÃ

¡¶Journal of Agriculture and Food Research¡·£ºFrom greenhouse production to consumer choices: the role of zinc biofortification and visual cues

¡¾×ÖÌ壺 ´ó ÖРС ¡¿ ʱ¼ä£º2026Äê06ÔÂ07ÈÕ À´Ô´£ºJournal of Agriculture and Food Research 6.2

±à¼­ÍƼö£º

¡¡¡¡ÐÂÏÊÊß²ËÁìÓòµÄ´´ÐÂÈÕÒæ½«¿É¼û²îÒ컯£¨ÈçÐÂÓ±ÑÕÉ«£©Óë¹ý³ÌÐÔ¸ÄÁ¼£¨Èç¿óÎïÖÊÉúÎïÇ¿»¯(biofortification)£©Ïà½áºÏ¡£µ±ÓªÑøÇ¿»¯ÎÞ·¨Í¨¹ýÊÓ¾õ¸Ð֪ʱ£¬Ïû·ÑÕß·´Ó¦È¡¾öÓÚ¾ö²ß¹ý³ÌÖпɼûÓë²»¿É¼ûÏßË÷µÄ¸ÐÖªÓë¼Ó¹¤·½Ê½¡£±¾Ñо¿¿¼²ìÁ˹ûʵÑÕÉ«£¨¿É¼ûÊôÐÔ£©Óëп(Zn)Éú

¡¡¡¡
ÐÂÏÊÊß²ËÁìÓòµÄ´´ÐÂÈÕÒæ½«¿É¼û²îÒ컯£¨ÈçÐÂÓ±ÑÕÉ«£©Óë¹ý³ÌÐÔ¸ÄÁ¼£¨Èç¿óÎïÖÊÉúÎïÇ¿»¯(biofortification)£©Ïà½áºÏ¡£µ±ÓªÑøÇ¿»¯ÎÞ·¨Í¨¹ýÊÓ¾õ¸Ð֪ʱ£¬Ïû·ÑÕß·´Ó¦È¡¾öÓÚ¾ö²ß¹ý³ÌÖпɼûÓë²»¿É¼ûÏßË÷µÄ¸ÐÖªÓë¼Ó¹¤·½Ê½¡£±¾Ñо¿¿¼²ìÁ˹ûʵÑÕÉ«£¨¿É¼ûÊôÐÔ£©Óëп(Zn)ÉúÎïÇ¿»¯£¨²»¿É¼ûµÄÐÅÈÎÆ·ÊôÐÔ(credence attribute)£©¹²Í¬¶ÔÐÂÏÊÃÔÄãÀî×Ó·¬ÇÑ(mini plum tomatoes)Æ«ºÃµÄÓ°Ï졣ͨ¹ýÎÂÊÒÊÔÑéÅçÊ©Zn-EDTAÑéÖ¤ÉúÎïÇ¿»¯ÊÇ·ñÓ°Ïì¿ÉÊÓ¾õ¸ÐÖªÐÔ×´£¬ÒÔÈ·±£Ïû·ÑÕßÑо¿µÄÄÚЧ¶È(internal validity)¡£²ÉÓÃʵÑéÊÒÕæÊµÑ¡ÔñÀëɢѡÔñʵÑé(real-choice discrete choice experiment, DCE, n=198)£¬½áºÏ°ó¶¨¹ºÂò¾ö²ßÓëÑÛ¶¯×·×Ù(eye-tracking)Êý¾Ý²¶×½ÊÓ¾õ×¢Ò⣬Òý³öÏû·ÑÕ߯«ºÃ¡£½á¹û±íÃ÷£ºÑÕÉ«ÊÇ×¢ÒâÁ¦ºÍÑ¡ÔñµÄÊ×ÒªÇý¶¯ÒòËØ£¬×غÖÉ«¼°»ìºÏÆ´×°±È´¿ºìɫѡÏîÎüÒý¸ü¶à×¢ÊÓ(gaze)ºÍ¹ºÂò£»Ç±Àà±ðÄ£ÐÍ(latent class model)ʶ±ð³öÈýÀàÏû·ÑÕß¡ª¡ªÁ½Àഴе¼ÏòȺÌåԸΪÉúÎïÇ¿»¯ºÍÐÂÑÕɫ֧¸¶Òç¼Û£¬Ò»Àà±£ÊØÈºÌå¶ÔÁ½ÊôÐÔ¾ùÑá¶ñ²¢±íÏÖÊÓ¾õ¹ýÔØ(visual overload)¼£Ïó¡£×ÛÉÏ£¬µ±ÐÅÈÎÆ·ÊôÐÔ²»Í¹ÏÔʱ¿É¼ûÏßË÷Ö÷µ¼¾ö²ß£¬ÑÛ¶¯×·×ÙÓÐÖúÓÚ½âÊÍÆ«ºÃÒìÖÊÐÔ(preference heterogeneity)¡£
ÂÛÎĽâ¶Á£º´ÓÎÂÊÒÉú²úµ½Ïû·ÑÕßÑ¡Ôñ¡ª¡ªÐ¿ÉúÎïÇ¿»¯ÓëÊÓ¾õÏßË÷ÔÚÃÔÄãÀî×Ó·¬ÇÑÖеÄ×÷ÓÃ
¸ÃÑо¿ÓÉMaria De Salvo¡¢Laura Giuffrida¡¢Marika Cerro¡¢Claudio Cannata¡¢Giovanni Signorello¡¢Giuseppe Cucuzza¡¢Rosario Paolo Mauro¼°Cherubino Leonardi¹²Í¬Íê³É£¬·¢±íÓÚ¡¶Journal of Agriculture and Food Research¡·¡£
Ò»¡¢Ñо¿±³¾°ÓëÒâÒå
ÏÊʳ԰ÒÕ²úÆ·´´Ð³£Éæ¼°Íâ¹Û¶àÑù»¯£¨ÈçÒýÈëйûÉ«£©ÓëÅ©ÒÕ¹ý³ÌÐÔÓªÑøÇ¿»¯£¨Èç΢Á¿ÔªËØÉúÎïÇ¿»¯(biofortification)£©¡£ÆäÖÐп(Zn)ÉúÎïÇ¿»¯¿ÉÓ¦¶Ô²¿·ÖÈËȺZnȱ·¦·çÏÕ£¬µ«ÆäÊôÐÅÈÎÆ·ÊôÐÔ(credence attribute)¡ª¡ª¹ºÂòǰºó¾ùÎÞ·¨Ö±½Ó¸ÐÖª¡£ÒÑÓÐÑо¿±íÃ÷Ò¶ÃæÊ©Zn¿ÉÄÜÓ°Ïì·¬ÇÑ´ÎÉú´úлÎµ«ÊÇ·ñ¸Ä±äÍâ¹ÛÈÔÓÐÕùÒ顣ͬʱ£¬Ïû·ÑÕß¶Ôº¬²»¿É¼ûÓªÑøÇ¿»¯¼°ÐÂÍâ¹Û·¬ÇѵÄÕæÊµÖ§¸¶ÒâÔ¸(willingness to pay, WTP)¡¢¿É¼ûÑÕÉ«ÓëÐÂÉ«Óë²»¿É¼ûÇ¿»¯ÈçºÎ¹²Í¬Ó°Ï칺Âò¾ö²ß£¬ÉÐȱ·¦»ùÓÚÕæÊµÑ¡ÔñʵÑé(real-choice DCE)²¢½áºÏÉúÀíÐÐΪ²âÊÔ£¨ÑÛ¶¯×·×Ù(eye-tracking)£©µÄÖ¤¾Ý¡£Îª´Ë£¬Ñо¿ÈËÔ±ÏȾ­ÎÂÊÒÊÔÑéÈ·ÈÏZnÉúÎïÇ¿»¯²»¸Ä±ä·¬ÇÑ¿ÉÊÓ¾õ¸ÐÖªÐÔ×´£¬ÔÙ¿ªÕ¹±ê¼Ç(labeled)ÕæÊµÑ¡ÔñÀëɢѡÔñʵÑ鲢ͬ²½²É¼¯ÑÛ¶¯Êý¾Ý£¬ÓÃDZÀà±ðÄ£ÐÍ(latent class model, LCM)·ÖÎöÆ«ºÃÒìÖÊÐÔ(preference heterogeneity)£¬Ì½Ìֿɼû£¨ÑÕÉ«£©Óë²»¿É¼û£¨ZnÉúÎïÇ¿»¯£©´´Ð¶ÔÏû·Ñ¾ö²ßµÄÓ°Ïì¡£
¶þ¡¢Ö÷Òª¹Ø¼ü¼¼Êõ·½·¨¸ÅÊö
Ñо¿²ÉÓÃÈý½×¶ÎÕûºÏ¿ò¼Ü£º¢ÙÎÂÊÒÊÔÑ顪¡ªÒÔ'Angelle'£¨ºì£©ºÍ'Dolcenera'£¨×غ֣©Á½¸öÃÔÄãÀî×Ó·¬ÇÑ(mini plum tomato)Æ·ÖÖΪ²ÄÁÏ£¬Éè¶ÔÕÕ£¨ÅçÕôÁóË®£©Óë´¦Àí×飨Åç1.7 mmol Zn L-1Zn-EDTA£©£¬Ëæ»úÇø×éÉè¼Æ£¬²â¶¨µ¥¹ûÖØ(FW)¡¢×Ý/ºá¾¶¡¢¹ûÐÎÖ¸Êý(L/D)¡¢É«¶È²ÎÊý(L, a, b*, Chroma, Hue½Ç)¼°¹ûʵZnº¬Á¿£¨»ðÑæÔ­×ÓÎüÊÕ¹âÆ×·¨£©£¬Ë«ÒòËØANOVA¼ìÑé´¦Àí¡Á»ùÒòÐÍЧӦ£»¢ÚʵÑéÊÒÕæÊµÑ¡ÔñÀëɢѡÔñʵÑé(DCE)¡ª¡ª½«ÎÂÊÒ²ú³ö·¬ÇÑװ͸Ã÷µ¥·Ý¹Þ£¨Ã¿¹Þ6¹û£©£¬ÉèÈý¸ö±ê¼ÇÑ¡Ïºì/רºÖ/»ìºÏÆ´×°£©¡¢ZnÉúÎïÇ¿»¯£¨ÓÐ/ÎÞ£¬±êÓÚ¹Þµ×±êÇ©£©ºÍÎå¸ö¼Û¸ñˮƽ(€0.99¨C€2.19)£¬º¬Í˳öÑ¡Ïî(no-purchase)£¬²ÎÓëÕß(n=198£¬Òâ´óÀû¿¨ËþÄáÑÇ´óѧʦÉúÖ°¹¤)ÓÀ5´ú½ðȯ×öÁ½´ÎÁ¬Ðø°ó¶¨¹ºÂò¾ö²ßÒÔ¼õÉÙ¼ÙÉèÆ«²î(hypothetical bias)£»¢ÛÑÛ¶¯×·×Ù¡ª¡ªÅå´÷Tobii Pro Glasses 2¼Ç¼£¬¶¨Òå¸÷²úÆ·¹Þ¼°±êǩΪÐËÈ¤Çø(AOI)£¬ÌáÈ¡×Ü×¢ÊÓʱ³¤(total fixation duration)×÷ΪÊÓ¾õ×¢ÒâÖ¸±ê£¬½«ÆäÄÉÈëLCMµÄÀà¹éÊôº¯Êý(class membership function)ÒÔ¼ìÑéÊÇ·ñ¸ÄÉÆÄ£ÐÍÄâºÏÓëWTPÚ¹ÊÍ¡£
Èý¡¢Ñо¿½á¹û
4.1. Fruit Zn content and main carpometric traits£¨¹ûʵпº¬Á¿ÓëÖ÷ÒªÐÎ̬ѧÐÔ×´£©
ÎÂÊÒÊý¾Ý·ÖÎöÏÔʾ£¬Zn-EDTA´¦Àíʹ¹ûʵZnº¬Á¿½Ï¶ÔÕÕÌá¸ß65£¥£¨186 vs. 113 ¦Ìg 100 g-1FW£¬P£¼0.05£©£¬µ«´¦ÀíÖ÷ЧӦ¼°´¦Àí¡Á»ùÒòÐÍ»¥×÷¶Ôµ¥¹ûÖØ¡¢×ݺᾶ¡¢¹ûÐÎÖ¸Êý¼°É«¶È²ÎÊý£¨L, a, b*, Chroma, Hue½Ç£©¾ùÎÞͳ¼ÆÑ§ÏÔÖøÓ°Ï죨¦¤E£¼1.0£¬µÍÓÚÈâÑۿɱæãÐÖµ£©¡£ÓÉ´Ë֤ʵÔÚ±¾ÊÔÑéÌõ¼þÏÂZnÉúÎïÇ¿»¯Êô´¿´â²»¿É¼ûÊôÐÔ£¬ÎªºóÐøDCEÖн«ÑÕÉ«ÓëÉúÎïÇ¿»¯×÷Ϊ¶ÀÁ¢ÊôÐÔÌṩũѧÒÀ¾Ý¡£
4.2. Behavioural and gaze analysis£¨ÐÐΪÓë×¢ÊÓ·ÖÎö£©
ÃèÊöÐÔͳ¼Æ·¢ÏÖ78£¥²ÎÓëÕßÖÁÉÙÑ¡¹ýÒ»´Îº¬×غÖÉ«·¬ÇѰü×°£¬82£¥ÖÁÉÙÒ»´ÎÑ¡ÔñÉúÎïÇ¿»¯²úÆ·£»×غּ°ºìר»ìºÏɫѡÏîÊ×ÂÖ¹ºÂòÂʸßÓÚ´¿ºì£¬µÚ¶þÂÖÆú¹º(no-purchase)±ÈÀýÉÏÉý£¬°µÊ¾Ñ°Çó¶àÑù»¯(variety-seeking)¡£Æ½¾ù»¨·Ñ€2.92£¨Õ¼Ô¤Ëã58.6%£©¡£×ÔÎÒ±¨¸æ±íÃ÷34£¥²ÎÓëÕßÖ»¹Ø×¢ÑÕÉ«¡¢18£¥Ö»¹Ø×¢ÉúÎïÇ¿»¯±êÇ©¡¢10£¥Ö»¹Ø×¢¼Û¸ñ£¬ÆäÓà²ÉÓò¹³¥ÐÔ¾ö²ß(compensatory decision-making)¡£ÑÛ¶¯Êý¾ÝʾÊ×´ÎÑ¡Ôñʱ×Ü×¢ÊÓʱ³¤¾ùÖµ29.17 s£¬¶þ´Î½µÖÁ12.98 s£¨Ñ§Ï°Ð§Ó¦£©£»×¢ÊÓ·ÖÅäÖ÷Òª¼¯ÖÐÓÚ²úÆ·£¨·¬ÇѹûʵÌ壩¶ø·Ç±êÇ©£¬Ó¡Ö¤¿É¼ûÏßË÷ÓÅÏȲ¶»ñ×¢Òâ¡£½«×¢ÊÓÖ¸±êÓë×ÔÊöÖØÒªÊôÐԱȶԣ¬¾­ÑéÌáÉý¶þÕßÒ»ÖÂÐÔ¡£Î»ÖÃÆ«²î(position bias)²¿·Ö´æÔÚ¡£
4.3. LC estimations£¨Ç±Àà±ðÄ£Ð͹À¼Æ£©
»ùÓÚBIC×îСԭÔòÑ¡¶¨ÈýÀà±ðLCM¡£»ùÏßÄ£Ðͼ°ÄÉÈë×¢ÊÓʱ³¤µÄÀ©Õ¹Ä£Ð;ùʶ±ð³ö£ºClass 1"´´Ðµ¼ÏòÏû·ÑÕß"(28£¥)¡ª¡ªZnÉúÎïÇ¿»¯WTP +€1.00£¨P£¼0.01£©£¬×غÖÉ«ASC +€2.20£¨P£¼0.001£©£¬»ìºÏÉ«ASC²»ÏÔÖø£¬Ç¿¸ºÏòÍ˳öÏClass 2"ÊÓ¾õÇý¶¯´´ÐÂÕß"(23£¥)¡ª¡ªZnÉúÎïÇ¿»¯WTP +€0.92£¨P£¼0.05£©£¬×غÖÉ«ASC +€0.93£¨P£¼0.05£©£¬»ìºÏÉ«ASC +€2.50£¨P£¼0.001£©£¬Ç¿¸ºÏòÍ˳öÏÇÒÀà¹éÊôÊÜ»ìºÏÉ«²úÆ·¹Þ¼°±êǩעÊÓʱ³¤ÕýÏòÓ°Ïì(¦Ã jar mixed = +150, P£¼0.001; ¦Ã label mixed = +44, P£¼0.001)£»Class 3"´«Í³Ïû·ÑÕß"(48£¥)¡ª¡ªZnÉúÎïÇ¿»¯WTP²»ÏÔÖø(0.00)£¬×غÖÉ«ASC ¨C€1.90£¨P£¼0.001£©£¬»ìºÏÉ«ASC ¨C€1.00£¨P£¼0.01£©£¬Ç¿¸ºÏòÍ˳öÏÀà¹éÊôÊÜרºÖÉ«±êÇ©³¤×¢ÊÓ¸ºÏòÓ°Ïì(¦Ã label brownish = ¨C180, P£¼0.10)¡£¼ÓÈëÑÛ¶¯Ö¸±êºóÄ£ÐÍAICÓÉ909½µÖÁ864£¬Ö¤ÊµÊÓ¾õ×¢Òâ²â¶È¿É½âÊÍδ¹Û²âÒìÖÊÐÔ²¢¸ÄÉÆLCMÄâºÏ¡£
ËÄ¡¢ÌÖÂÛÓë½áÂÛ×ܽá
ÌÖÂÛÖ¸³ö£¬Ò¶ÃæZn-EDTA¿ÉÓÐЧÌáÉýÃÔÄãÀî×Ó·¬ÇÑZnº¬Á¿¶ø²»¸ÄÍâ¹Û£¬Ö§³ÖÒÔ±êÇ©µ¥¶À´«´ïÉúÎïÇ¿»¯ÐÅÏ¢¡£Ïû·ÑÕßÑ¡ÔñÖпɼûÑÕÉ«£¨ÓÈÆäרºÖ¼°»ìºÏÆ´×°£©Ö÷µ¼³õ²½×¢ÒâÓ빺Âò£¬ZnÉúÎïÇ¿»¯Ëä´ÎÒªµ«ÔÚ´´Ðµ¼Ïòϸ·ÖÊг¡¾ßÕýÏòWTP¡£ÑÛ¶¯ÕûºÏLCM½Òʾ²»Í¬Ï¸·ÖȺ¾ß²îÒ컯µÄÊÓ¾õ¼Ó¹¤Ä£Ê½¡£Ñо¿¾ÖÏÞº¬ÊµÑéÔ¤Ëã¿ÉÄÜÒý·¢ÐÄÀíÕË»§(mental accounting)ÖÂWTPÆ«¸ß¡¢"biofortified"ÊõÓïDZÔÚп־åÖ¢(neophobia)¼°ÑÛ¶¯Î´º­¸ÇÉî²ãÈÏÖªÇé¸Ð¡£
½áÂÛ·­ÒëÈçÏ£º±¾Ñо¿Í¨¹ýÎÂÊÒÊÔÑéÓëÐÐΪʵÑé×ÛºÏ̽Îö¿É¼û£¨¹ûÉ«£©Óë²»¿É¼û£¨ZnÉúÎïÇ¿»¯£©ÊôÐÔ¡¢ÊÓ¾õ×¢Òâ¼°Æ«ºÃÒìÖÊÐÔÔÚÏÊʳ·¬ÇÑÑ¡ÔñÖеÄ×÷Óá£ZnÉúÎïÇ¿»¯²»Ó°Ïì¿ÉÊÓ¾õ¸ÐÖª¹ûʵÐÔ×´£¬È·Ö¤ÆäΪ²»¿É¼ûÊôÐÔ£¨RQ1£©¡£Ïû·ÑÕßÑ¡ÔñÊ×ÒªÊܿɼûÊôÐÔÓÈÆäÊÇÑÕÉ«Çý¶¯£¬ÉúÎïÇ¿»¯Æð´ÎÒªµ«Ïà¹Ø×÷Óã¨RQ2£©¡£ÊÓ¾õ×¢Òâ¹ØÁªÑ¡ÔñÐÐΪµ«·Ç¾ö¶¨Òò×Ó£¬×¢ÊÓģʽÓÐÖúÀí½â¾ö²ß¹ý³Ì£¨RQ3£©¡£½«ÑÛ¶¯Ö¸±êÄÉÈëLCM¸ÄÉÆÆ«ºÃÒìÖÊÐÔÚ¹ÊͲ¢ÌáÉýÄ£ÐÍÄâºÏ£¬Ö§³ÖÆäÔÚÀëɢѡÔñ½¨Ä£ÖеÄÓ¦ÓüÛÖµ£¨RQ4£©¡£²úÆ·¶ËZn·ÊÊ©ÓÃÄÜÌá¹ûʵZnÇÒ±£Íâ¹ÛÆ·ÖÊ£»ÓªÏú¶Ë»ìɫƴװ¿É×÷Ö÷´òÊÓ¾õÎüÒýµã£¬¶ÔÊÓ¾õÇý¶¯´´ÐÂȺÌåÓÈΪÓÐЧ£¬¶ø¶Ô´«Í³Ïû·ÑÕßÐè¸üÇåÎú±êÇ©¹µÍ¨¡£¿É¼ûÓë²»¿É¼ûÊôÐÔͨ¹ýÆäÏà¶ÔÖØÒªÐÔ£¨·ÇÐÎʽ½»»¥Ï¹²Í¬ËÜÔìÏû·ÑÕßÑ¡Ôñ£¬½á¹û¿ÉΪũ²úÆ·´´ÐÂÉè¼Æ¡¢±êÇ©²ßÂÔ¼°ÉæÅ©Õþ²ßÖÆ¶¨ÌṩÒÀ¾Ý¡£
Ïà¹ØÐÂÎÅ
ÉúÎïͨ΢ÐŹ«ÖÚºÅ
΢ÐÅ
ÐÂÀË΢²©

ÈȵãÅÅÐÐ

    ½ñÈÕ¶¯Ì¬ | È˲ÅÊг¡ | м¼ÊõרÀ¸ | Öйú¿ÆÑ§ÈË | ÔÆÕ¹Ì¨ | BioHot | ÔÆ½²ÌÃÖ±²¥ | »áÕ¹ÖÐÐÄ | ÌØ¼ÛרÀ¸ | ¼¼Êõ¿ìѶ | Ãâ·ÑÊÔÓÃ

    °æÈ¨ËùÓÐ ÉúÎïͨ

    Copyright© eBiotrade.com, All Rights Reserved

    ÁªÏµÐÅÏ䣺

    ÔÁICP±¸09063491ºÅ